R/StatsAndCIs.r
In AndriSignorell/DescTools: Tools for Descriptive Statistics
Defines functions
fn
fn
BinomRatioCI_old
BinomDiffCI
BinomCIn
BinomCI
BootCI
LOF
Outlier
HodgesLehmann
HuberM
.huberM
.wgt.himedian
.tauHuber
TukeyBiweight
Hmean
Mode
MESS_auc
AUC
FindCorr
print.CorPolychor
CorCI
FisherZInv
FisherZ
CorPart
Quantile
Median.Freq
Median.factor
Median.default
Median
Mgsub
VarX
EX
VarN
SDN
Var.Freq
MADCI
MAD
.NormWeights
Documented in
AUC
BinomCI
BinomCIn
BinomDiffCI
BootCI
CorCI
CorPart
EX
FindCorr
FisherZ
FisherZInv
Hmean
HodgesLehmann
HuberM
LOF
MAD
MADCI
Median
Median.default
Median.factor
Median.Freq
Mgsub
Mode
Outlier
print.CorPolychor
Quantile
SDN
TukeyBiweight
Var.Freq
VarN
VarX
# Project: DescTools
# Chapter: Statistical functions and confidence intervals
#
# Purpose: Tools for descriptive statistics, the missing link...
# Univariat, pairwise bivariate, groupwise und multivariate
#
# Author: Andri Signorell
# Version: 0.99.x
#
# some aliases
# internal function, no use to export it.. (?)
.NormWeights <- function(x, weights, na.rm=FALSE, zero.rm=FALSE, normwt=FALSE) {
# Idea Henrik Bengtsson
# we remove values with zero (and negative) weight.
# This would:
# 1) take care of the case when all weights are zero,
# 2) it will most likely speed up the sorting.
if (na.rm){
keep <- !is.na(x) & !is.na(weights)
if(zero.rm)
# remove values with zero weights
keep <- keep & (weights > 0)
x <- x[keep]
weights <- weights[keep]
}
if(any(is.na(x)) | (!is.null(weights) & any(is.na(weights))))
return(NA_real_)
n <- length(x)
if (length(weights) != n)
stop("length of 'weights' must equal the number of rows in 'x'")
# x and weights have length=0
if(length(x)==0)
return(list(x = x, weights = x, wsum = NaN))
if (any(weights< 0) || (s <- sum(weights)) == 0)
stop("weights must be non-negative and not all zero")
# we could normalize the weights to sum up to 1
if (normwt)
weights <- weights * n/s
return(list(x=x, weights=as.double(weights), wsum=s))
}
MAD <- function(x, weights = NULL, center = Median, constant = 1.4826, na.rm = FALSE,
low = FALSE, high = FALSE) {
if (is.function(center)) {
fct <- center
center <- "fct"
if(is.null(weights))
center <- gettextf("%s(x)", center)
else
center <- gettextf("%s(x, weights=weights)", center)
center <- eval(parse(text = center))
}
if(!is.null(weights)) {
z <- .NormWeights(x, weights, na.rm=na.rm, zero.rm=TRUE)
res <- constant * Median(abs(z$x - center), weights = z$weights)
} else {
# fall back to mad(), if there are no weights
res <- mad(x, center = center, constant = constant, na.rm = na.rm, low=low, high=high)
}
return(res)
}
MADCI <- function(x, y = NULL, two.samp.diff = TRUE, gld.est = "TM",
conf.level = 0.95, sides = c("two.sided","left","right"),
na.rm = FALSE, ...) {
if (na.rm) x <- na.omit(x)
sides <- match.arg(sides, choices = c("two.sided","left","right"),
several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
asv.mad <- function(x, method = "TM"){
lambda <- fit.fkml(x, method = method)$lambda
m <- median(x)
mad.x <- mad(x)
fFinv <- dgl(c(m - mad.x, m + mad.x, m), lambda1 = lambda)
FFinv <- pgl(c(m - mad.x, m + mad.x), lambda1 = lambda)
A <- fFinv[1] + fFinv[2]
C <- fFinv[1] - fFinv[2]
B <- C^2 + 4*C*fFinv[3]*(1 - FFinv[2] - FFinv[1])
(1/(4 * A^2))*(1 + B/fFinv[3]^2)
}
alpha <- 1 - conf.level
z <- qnorm(1 - alpha/2)
est <- mad.x <- mad(x)
n.x <- length(x)
asv.x <- asv.mad(x, method = gld.est)
if(is.null(y)){
ci <- mad.x + c(-z, z) * sqrt(asv.x / n.x)
} else{
y <- y[!is.na(y)]
mad.y <- mad(y)
n.y <- length(y)
asv.y <- asv.mad(y, method = gld.est)
if(two.samp.diff){
est <- mad.x - mad.y
ci <- est + c(-z, z)*sqrt(asv.x/n.x + asv.y/n.y)
} else{
est <- (mad.x/mad.y)^2
log.est <- log(est)
var.est <- 4 * est * ((1/mad.y^2)*asv.x/n.x + (est/mad.y^2)*asv.y/n.y)
Var.log.est <- (1 / est^2) * var.est
ci <- exp(log.est + c(-z, z) * sqrt(Var.log.est))
}
}
res <- c(est, ci)
names(res) <- c("mad","lwr.ci","upr.ci")
if(sides=="left")
res[3] <- Inf
else if(sides=="right")
res[2] <- -Inf
return( res )
}
# from stats
SD <- function (x, weights = NULL, na.rm = FALSE, ...)
sqrt(Var(if (is.vector(x) || is.factor(x)) x else as.double(x),
weights=weights, na.rm = na.rm, ...))
Var <- function (x, ...)
UseMethod("Var")
Var.default <- function (x, weights = NULL, na.rm = FALSE, method = c("unbiased", "ML"), ...) {
if(is.null(weights)) {
res <- var(x=x, na.rm=na.rm)
} else {
z <- .NormWeights(x, weights, na.rm=na.rm, zero.rm=TRUE)
if (match.arg(method) == "ML")
return(as.numeric(stats::cov.wt(cbind(z$x), z$weights, method = "ML")$cov))
xbar <- sum(z$weights * x) / z$wsum
res <- sum(z$weights * ((z$x - xbar)^2))/(z$wsum - 1)
}
return(res)
}
Var.Freq <- function(x, breaks, ...) {
n <- sum(x$freq)
mu <- sum(head(MoveAvg(breaks, order=2, align="left"), -1) * x$perc)
s2 <- (sum(head(MoveAvg(breaks, order=2, align="left"), -1)^2 * x$freq) - n*mu^2) / (n-1)
return(s2)
}
Cov <- cov
Cor <- cor
SDN <- function(x, na.rm = FALSE){
sd(x, na.rm=na.rm) * sqrt(((n <- sum(!is.na(x)))-1) /n)
}
VarN <- function(x, na.rm = FALSE){
var(x, na.rm=na.rm) * ((n <- sum(!is.na(x)))-1) /n
}
EX <- function(x, p) sum(x * p)
VarX <- function(x, p) sum((x - EX(x, p))^2 * p)
# multiple gsub
Mgsub <- function(pattern, replacement, x, ...) {
if (length(pattern)!=length(replacement)) {
stop("pattern and replacement do not have the same length.")
}
result <- x
for (i in 1:length(pattern)) {
result <- gsub(pattern[i], replacement[i], result, ...)
}
result
}
# Length(x)
# Table(x)
# Log(x)
# Abs(x)
#
# "abs", "sign", "sqrt", "ceiling", "floor", "trunc", "cummax", "cummin", "cumprod", "cumsum",
# "log", "log10", "log2", "log1p", "acos", "acosh", "asin", "asinh", "atan", "atanh",
# "exp", "expm1", "cos", "cosh", "cospi", "sin", "sinh", "sinpi", "tan", "tanh",
# "tanpi", "gamma", "lgamma", "digamma", "trigamma"
Median <- function(x, ...)
UseMethod("Median")
Median.default <- function(x, weights = NULL, na.rm = FALSE, ...) {
if(is.null(weights))
median(x=x, na.rm=na.rm)
else
Quantile(x, weights, probs=0.5, na.rm=na.rm, names=FALSE)
}
# ordered interface for the median
Median.factor <- function(x, na.rm = FALSE, ...) {
# Answered by Hong Ooi on 2011-10-28T00:37:08-04:00
# http://www.rqna.net/qna/nuiukm-idiomatic-method-of-finding-the-median-of-an-ordinal-in-r.html
# return NA, if x is not ordered
# clearme: why not median.ordered?
if(!is.ordered(x)) return(NA)
if(na.rm) x <- na.omit(x)
if(any(is.na(x))) return(NA)
levs <- levels(x)
m <- median(as.integer(x), na.rm = na.rm)
if(floor(m) != m)
{
warning("Median is between two values; using the first one")
m <- floor(m)
}
ordered(m, labels = levs, levels = seq_along(levs))
}
Median.Freq <- function(x, breaks, ...) {
mi <- min(which(x$cumperc > 0.5))
breaks[mi] + (tail(x$cumfreq, 1)/2 - x[mi-1, "cumfreq"]) /
x[mi, "freq"] * diff(breaks[c(mi, mi+1)])
}
# further weighted quantiles in Hmisc and modi, both on CRAN
Quantile <- function(x, weights = NULL, probs = seq(0, 1, 0.25),
na.rm = FALSE, names=TRUE, type = 7, digits=7) {
if(is.null(weights)){
quantile(x=x, probs=probs, na.rm=na.rm, names=names, type=type, digits=digits)
} else {
# this is a not exported stats function
format_perc <- function (x, digits = max(2L, getOption("digits")), probability = TRUE,
use.fC = length(x) < 100, ...) {
if (length(x)) {
if (probability)
x <- 100 * x
ans <- paste0(if (use.fC)
formatC(x, format = "fg", width = 1, digits = digits)
else format(x, trim = TRUE, digits = digits, ...), "%")
ans[is.na(x)] <- ""
ans
}
else character(0)
}
sorted <- FALSE
# initializations
if (!is.numeric(x)) stop("'x' must be a numeric vector")
n <- length(x)
if (n == 0 || (!isTRUE(na.rm) && any(is.na(x)))) {
# zero length or missing values
return(rep.int(NA, length(probs)))
}
if (!is.null(weights)) {
if (!is.numeric(weights)) stop("'weights' must be a numeric vector")
else if (length(weights) != n) {
stop("'weights' must have the same length as 'x'")
} else if (!all(is.finite(weights))) stop("missing or infinite weights")
if (any(weights < 0)) warning("negative weights")
if (!is.numeric(probs) || all(is.na(probs)) ||
isTRUE(any(probs < 0 | probs > 1))) {
stop("'probs' must be a numeric vector with values in [0,1]")
}
if (all(weights == 0)) { # all zero weights
warning("all weights equal to zero")
return(rep.int(0, length(probs)))
}
}
# remove NAs (if requested)
if(isTRUE(na.rm)){
indices <- !is.na(x)
x <- x[indices]
n <- length(x)
if(!is.null(weights)) weights <- weights[indices]
}
# sort values and weights (if requested)
if(!isTRUE(sorted)) {
# order <- order(x, na.last=NA) ## too slow
order <- order(x)
x <- x[order]
weights <- weights[order] # also works if 'weights' is NULL
}
# some preparations
if(is.null(weights)) rw <- (1:n)/n
else rw <- cumsum(weights)/sum(weights)
# obtain quantiles
# currently only type 5
if (type == 5) {
qs <- sapply(probs,
function(p) {
if (p == 0) return(x[1])
else if (p == 1) return(x[n])
select <- min(which(rw >= p))
if(rw[select] == p) mean(x[select:(select+1)])
else x[select]
})
} else if(type == 7){
if(is.null(weights)){
index <- 1 + max(n - 1, 0) * probs
lo <- pmax(floor(index), 1)
hi <- ceiling(index)
x <- sort(x, partial = if (n == 0)
numeric()
else unique(c(lo, hi)))
qs <- x[lo]
i <- which((index > lo & x[hi] != qs))
h <- (index - lo)[i]
qs[i] <- (1 - h) * qs[i] + h * x[hi[i]]
} else {
n <- sum(weights)
ord <- 1 + (n - 1) * probs
low <- pmax(floor(ord), 1)
high <- pmin(low + 1, n)
ord <- ord %% 1
## Find low and high order statistics
## These are minimum values of x such that the cum. freqs >= c(low,high)
allq <- approx(cumsum(weights), x, xout=c(low, high),
method='constant', f=1, rule=2)$y
k <- length(probs)
qs <- (1 - ord)*allq[1:k] + ord*allq[-(1:k)]
}
} else {
qs <- NA
warning(gettextf("type %s is not implemented", type))
}
# return(unname(q))
# why unname? change to named.. 14.10.2020
if (names && length(probs) > 0L) {
stopifnot(is.numeric(digits), digits >= 1)
names(qs) <- format_perc(probs, digits = digits)
}
return(qs)
}
}
IQRw <- function (x, weights = NULL, na.rm = FALSE, type = 7) {
if(is.null(weights))
IQR(x=x, na.rm=na.rm, type=type)
else
diff(Quantile(x, weights=weights, probs=c(0.25, 0.75), na.rm=na.rm, type=type))
}
## stats: functions (RobRange, Hmean, Gmean, Aad, HuberM etc.) ====
CorPart <- function(m, x, y) {
cl <- match.call()
if(dim(m)[1] != dim(m)[2]) {
n.obs <- dim(m)[1]
m <- cor(m, use="pairwise")
}
if(!is.matrix(m)) m <- as.matrix(m)
# first reorder the matrix to select the right variables
nm <- dim(m)[1]
t.mat <- matrix(0, ncol=nm, nrow=nm)
xy <- c(x,y)
numx <- length(x)
numy <- length(y)
nxy <- numx+numy
for (i in 1:nxy) {
t.mat[i, xy[i]] <- 1
}
reorder <- t.mat %*% m %*% t(t.mat)
reorder[abs(reorder) > 1] <- NA # this allows us to use the matrix operations to reorder and pick
X <- reorder[1:numx, 1:numx]
Y <- reorder[1:numx, (numx+1):nxy]
phi <- reorder[(numx+1):nxy,(numx+1):nxy]
phi.inv <- solve(phi)
X.resid <- X - Y %*% phi.inv %*% t(Y)
sd <- diag(sqrt(1/diag(X.resid)))
X.resid <- sd %*% X.resid %*% sd
colnames(X.resid) <- rownames(X.resid) <- colnames(m)[x]
return(X.resid)
}
FisherZ <- function(rho) {0.5*log((1+rho)/(1-rho)) } #converts r to z
FisherZInv <- function(z) {(exp(2*z)-1)/(1+exp(2*z)) } #converts back again
CorCI <- function(rho, n, conf.level = 0.95, alternative = c("two.sided","less","greater")) {
if (n < 3L)
stop("not enough finite observations")
if (!missing(conf.level) && (length(conf.level) != 1 || !is.finite(conf.level)
|| conf.level < 0 || conf.level > 1))
stop("'conf.level' must be a single number between 0 and 1")
alternative <- match.arg(alternative)
# correct rho == 1 with rho == almost 1 in order to return ci = c(1, 1)
# which is a sensible value for the confidence interval
if(identical(rho, 1))
ci <- c(1, 1)
else {
z <- FisherZ(rho)
sigma <- 1/sqrt(n - 3)
ci <- switch(alternative,
less = c(-Inf, z + sigma * qnorm(conf.level)),
greater = c(z - sigma * qnorm(conf.level), Inf),
two.sided = z + c(-1, 1) * sigma * qnorm((1 + conf.level)/2))
ci <- FisherZInv(ci)
}
return(c(cor = rho, lwr.ci = ci[1], upr.ci = ci[2]))
}
CorPolychor <- function (x, y, ML=FALSE, control=list(), std.err=FALSE, maxcor=.9999){
# last modified 21 Oct 08 by J. Fox
binBvn <- function(rho, row.cuts, col.cuts, bins=4){
# last modified 29 Mar 07 by J. Fox
row.cuts <- if (missing(row.cuts)) c(-Inf, 1:(bins - 1)/bins, Inf) else c(-Inf, row.cuts, Inf)
col.cuts <- if (missing(col.cuts)) c(-Inf, 1:(bins - 1)/bins, Inf) else c(-Inf, col.cuts, Inf)
r <- length(row.cuts) - 1
c <- length(col.cuts) - 1
P <- matrix(0, r, c)
R <- matrix(c(1, rho, rho, 1), 2, 2)
for (i in 1:r){
for (j in 1:c){
P[i,j] <- pmvnorm(lower=c(row.cuts[i], col.cuts[j]),
upper=c(row.cuts[i+1], col.cuts[j+1]),
corr=R)
}
}
P
}
f <- function(pars) {
if (length(pars) == 1){
rho <- pars
if (abs(rho) > maxcor) rho <- sign(rho)*maxcor
row.cuts <- rc
col.cuts <- cc
}
else {
rho <- pars[1]
if (abs(rho) > maxcor) rho <- sign(rho)*maxcor
row.cuts <- pars[2:r]
col.cuts <- pars[(r+1):(r+c-1)]
}
P <- binBvn(rho, row.cuts, col.cuts)
- sum(tab * log(P))
}
tab <- if (missing(y)) x else table(x, y)
zerorows <- apply(tab, 1, function(x) all(x == 0))
zerocols <- apply(tab, 2, function(x) all(x == 0))
zr <- sum(zerorows)
if (0 < zr) warning(paste(zr, " row", suffix <- if(zr == 1) "" else "s",
" with zero marginal", suffix," removed", sep=""))
zc <- sum(zerocols)
if (0 < zc) warning(paste(zc, " column", suffix <- if(zc == 1) "" else "s",
" with zero marginal", suffix, " removed", sep=""))
tab <- tab[!zerorows, ,drop=FALSE]
tab <- tab[, !zerocols, drop=FALSE]
r <- nrow(tab)
c <- ncol(tab)
if (r < 2) {
warning("the table has fewer than 2 rows")
return(NA)
}
if (c < 2) {
warning("the table has fewer than 2 columns")
return(NA)
}
n <- sum(tab)
rc <- qnorm(cumsum(rowSums(tab))/n)[-r]
cc <- qnorm(cumsum(colSums(tab))/n)[-c]
if (ML) {
result <- optim(c(optimise(f, interval=c(-1, 1))$minimum, rc, cc), f,
control=control, hessian=std.err)
if (result$par[1] > 1){
result$par[1] <- 1
warning("inadmissible correlation set to 1")
}
else if (result$par[1] < -1){
result$par[1] <- -1
warning("inadmissible correlation set to -1")
}
if (std.err) {
chisq <- 2*(result$value + sum(tab * log((tab + 1e-6)/n)))
df <- length(tab) - r - c
result <- list(type="polychoric",
rho=result$par[1],
row.cuts=result$par[2:r],
col.cuts=result$par[(r+1):(r+c-1)],
var=solve(result$hessian),
n=n,
chisq=chisq,
df=df,
ML=TRUE)
class(result) <- "polycor"
return(result)
}
else return(as.vector(result$par[1]))
}
else if (std.err){
result <- optim(0, f, control=control, hessian=TRUE, method="BFGS")
if (result$par > 1){
result$par <- 1
warning("inadmissible correlation set to 1")
}
else if (result$par < -1){
result$par <- -1
warning("inadmissible correlation set to -1")
}
chisq <- 2*(result$value + sum(tab *log((tab + 1e-6)/n)))
df <- length(tab) - r - c
result <- list(type="polychoric",
rho=result$par,
var=1/result$hessian,
n=n,
chisq=chisq,
df=df,
ML=FALSE)
class(result) <- "CorPolychor"
return(result)
}
else optimise(f, interval=c(-1, 1))$minimum
}
print.CorPolychor <- function(x, digits = max(3, getOption("digits") - 3), ...){
# last modified 24 June 04 by J. Fox
if (x$type == "polychoric"){
se <- sqrt(diag(x$var))
se.rho <- se[1]
est <- if (x$ML) "ML est." else "2-step est."
cat("\nPolychoric Correlation, ", est, " = ", signif(x$rho, digits),
" (", signif(se.rho, digits), ")", sep="")
if (x$df > 0)
cat("\nTest of bivariate normality: Chisquare = ",
signif(x$chisq, digits), ", df = ", x$df, ", p = ",
signif(pchisq(x$chisq, x$df, lower.tail=FALSE), digits), "\n", sep="")
else cat("\n")
r <- length(x$row.cuts)
c <- length(x$col.cuts)
if (r == 0) return(invisible(x))
row.cuts.se <- se[2:(r+1)]
col.cuts.se <- se[(r+2):(r+c+1)]
rowThresh <- signif(cbind(x$row.cuts, row.cuts.se), digits)
if (r > 1) cat("\n Row Thresholds\n")
else cat("\n Row Threshold\n")
colnames(rowThresh) <- c("Threshold", "Std.Err.")
rownames(rowThresh) <- if (r > 1) 1:r else " "
print(rowThresh)
colThresh <- signif(cbind(x$col.cuts, col.cuts.se), digits)
if (c > 1) cat("\n\n Column Thresholds\n")
else cat("\n\n Column Threshold\n")
colnames(colThresh) <- c("Threshold", "Std.Err.")
rownames(colThresh) <- if (c > 1) 1:c else " "
print(colThresh)
}
else if (x$type == "polyserial"){
se <- sqrt(diag(x$var))
se.rho <- se[1]
est <- if (x$ML) "ML est." else "2-step est."
cat("\nPolyserial Correlation, ", est, " = ", signif(x$rho, digits),
" (", signif(se.rho, digits), ")", sep="")
cat("\nTest of bivariate normality: Chisquare = ", signif(x$chisq, digits),
", df = ", x$df, ", p = ", signif(pchisq(x$chisq, x$df, lower.tail=FALSE), digits),
"\n\n", sep="")
if (length(se) == 1) return(invisible(x))
cuts.se <- se[-1]
thresh <- signif(rbind(x$cuts, cuts.se), digits)
colnames(thresh) <- 1:length(x$cuts)
rownames(thresh) <- c("Threshold", "Std.Err.")
print(thresh)
}
else print(unclass(x))
invisible(x)
}
FindCorr <- function(x, cutoff = .90, verbose = FALSE) {
# Author: Max Kuhn
# source library(caret)
varnum <- dim(x)[1]
if(!isTRUE(all.equal(x, t(x)))) stop("correlation matrix is not symmetric")
if(varnum ==1) stop("only one variable given")
x <- abs(x)
# re-ordered columns based on max absolute correlation
originalOrder <- 1:varnum
averageCorr <- function(x) mean(x, na.rm = TRUE)
tmp <- x
diag(tmp) <- NA
maxAbsCorOrder <- order(apply(tmp, 2, averageCorr), decreasing = TRUE)
x <- x[maxAbsCorOrder, maxAbsCorOrder]
newOrder <- originalOrder[maxAbsCorOrder]
deletecol <- 0
for(i in 1L:(varnum-1))
{
for(j in (i+1):varnum)
{
if(!any(i == deletecol) & !any(j == deletecol))
{
if(verbose)
cat("Considering row\t", newOrder[i],
"column\t", newOrder[j],
"value\t", round(x[i,j], 3), "\n")
if(abs(x[i,j]) > cutoff)
{
if(mean(x[i, -i]) > mean(x[-j, j]))
{
deletecol <- unique(c(deletecol, i))
if(verbose) cat(" Flagging column\t", newOrder[i], "\n")
} else {
deletecol <- unique(c(deletecol, j))
if(verbose) cat(" Flagging column\t", newOrder[j], "\n")
}
}
}
}
}
deletecol <- deletecol[deletecol != 0]
newOrder[deletecol]
}
# Alternative:
# From roc bioconductor
# Vince Carey (stvjc@channing.harvard.edu)
# trapezint <- function (x, y, a, b){
#
# if (length(x) != length(y))
# stop("length x must equal length y")
# y <- y[x >= a & x <= b]
# x <- x[x >= a & x <= b]
# if (length(unique(x)) < 2)
# return(NA)
# ya <- approx(x, y, a, ties = max, rule = 2)$y
# yb <- approx(x, y, b, ties = max, rule = 2)$y
# x <- c(a, x, b)
# y <- c(ya, y, yb)
# h <- diff(x)
# lx <- length(x)
# 0.5 * sum(h * (y[-1] + y[-lx]))
# }
# AUC_deprecated <- function(x, y, from=min(x, na.rm=TRUE), to = max(x, na.rm=TRUE),
# method=c("trapezoid", "step", "spline", "linear"),
# absolutearea = FALSE, subdivisions = 100, na.rm = FALSE, ...) {
#
# # calculates Area unter the curve
# # example:
# # AUC( x=c(1,2,3,5), y=c(0,1,1,2))
# # AUC( x=c(2,3,4,5), y=c(0,1,1,2))
#
# if(na.rm) {
# idx <- complete.cases(cbind(x,y))
# x <- x[idx]
# y <- y[idx]
# }
#
# if (length(x) != length(y))
# stop("length x must equal length y")
#
# idx <- order(x)
# x <- x[idx]
# y <- y[idx]
#
# switch( match.arg( arg=method, choices=c("trapezoid","step","spline","linear") )
# , "trapezoid" = { a <- sum((apply( cbind(y[-length(y)], y[-1]), 1, mean))*(x[-1] - x[-length(x)])) }
# , "step" = { a <- sum( y[-length(y)] * (x[-1] - x[-length(x)])) }
# , "linear" = {
# a <- MESS_auc(x, y, from = from , to = to, type="linear",
# absolutearea=absolutearea, subdivisions=subdivisions, ...)
# }
# , "spline" = {
# a <- MESS_auc(x, y, from = from , to = to, type="spline",
# absolutearea=absolutearea, subdivisions=subdivisions, ...)
# # a <- integrate(splinefun(x, y, method="natural"), lower=min(x), upper=max(x))$value
# }
# )
# return(a)
# }
# New version, publish as soon as package sobir is updated
AUC <- function(x, y, from = min(x, na.rm=TRUE), to = max(x, na.rm=TRUE),
method=c("trapezoid", "step", "spline", "linear"), absolutearea = FALSE,
subdivisions = 100, na.rm = FALSE, ...) {
if(identical(method, "linear")){
warning("method linear is no longer supported!")
return(NA)
}
# calculates Area unter the curve
# example:
# AUC( x=c(1,2,3,5), y=c(0,1,1,2))
# AUC( x=c(2,3,4,5), y=c(0,1,1,2))
if(na.rm) {
idx <- complete.cases(cbind(x,y))
x <- x[idx]
y <- y[idx]
}
if (length(x) != length(y))
stop("length x must equal length y")
if (length(x) < 2)
return(NA)
o <- order(x)
x <- x[o]
y <- y[o]
ox <- x[o]
oy <- y[o]
method <- match.arg(method)
if (method=="trapezoid") {
# easy and short
# , "trapezoid" = { a <- sum((apply( cbind(y[-length(y)], y[-1]), 1, mean))*(x[-1] - x[-length(x)])) }
## Default option
if (!absolutearea) {
values <- approx(x, y, xout = sort(unique(c(from, to, x[x > from & x < to]))), ...)
res <- 0.5 * sum(diff(values$x) * (values$y[-1] + values$y[-length(values$y)]))
} else { ## Absolute areas
idx <- which(diff(oy >= 0)!=0)
newx <- c(x, x[idx] - oy[idx]*(x[idx+1]-x[idx]) / (y[idx+1]-y[idx]))
newy <- c(y, rep(0, length(idx)))
values <- approx(newx, newy, xout = sort(unique(c(from, to, newx[newx > from & newx < to]))), ...)
res <- 0.5 * sum(diff(values$x) * (abs(values$y[-1]) + abs(values$y[-length(values$y)])))
}
} else if (method=="step") {
# easy and short
# , "step" = { a <- sum( y[-length(y)] * (x[-1] - x[-length(x)])) }
## Default option
if (!absolutearea) {
values <- approx(x, y, xout = sort(unique(c(from, to, x[x > from & x < to]))), ...)
res <- sum(diff(values$x) * values$y[-length(values$y)])
# res <- sum( y[-length(y)] * (x[-1] - x[-length(x)]))
} else { ## Absolute areas
idx <- which(diff(oy >= 0)!=0)
newx <- c(x, x[idx] - oy[idx]*(x[idx+1]-x[idx]) / (y[idx+1]-y[idx]))
newy <- c(y, rep(0, length(idx)))
values <- approx(newx, newy, xout = sort(unique(c(from, to, newx[newx > from & newx < to]))), ...)
res <- sum(diff(values$x) * abs(values$y[-length(values$y)]))
}
} else if (method=="spline") {
if (absolutearea)
myfunction <- function(z) { abs(splinefun(x, y, method="natural")(z)) }
else
myfunction <- splinefun(x, y, method="natural")
res <- integrate(myfunction, lower=from, upper=to, subdivisions=subdivisions)$value
}
return(res)
}
MESS_auc <- function(x, y, from = min(x, na.rm=TRUE), to = max(x, na.rm=TRUE), type=c("linear", "spline"),
absolutearea=FALSE, subdivisions =100, ...) {
type <- match.arg(type)
# Sanity checks
stopifnot(length(x) == length(y))
stopifnot(!is.na(from))
if (length(unique(x)) < 2)
return(NA)
if (type=="linear") {
## Default option
if (absolutearea==FALSE) {
values <- approx(x, y, xout = sort(unique(c(from, to, x[x > from & x < to]))), ...)
res <- 0.5 * sum(diff(values$x) * (values$y[-1] + values$y[-length(values$y)]))
} else { ## Absolute areas
## This is done by adding artificial dummy points on the x axis
o <- order(x)
ox <- x[o]
oy <- y[o]
idx <- which(diff(oy >= 0)!=0)
newx <- c(x, x[idx] - oy[idx]*(x[idx+1]-x[idx]) / (y[idx+1]-y[idx]))
newy <- c(y, rep(0, length(idx)))
values <- approx(newx, newy, xout = sort(unique(c(from, to, newx[newx > from & newx < to]))), ...)
res <- 0.5 * sum(diff(values$x) * (abs(values$y[-1]) + abs(values$y[-length(values$y)])))
}
} else { ## If it is not a linear approximation
if (absolutearea)
myfunction <- function(z) { abs(splinefun(x, y, method="natural")(z)) }
else
myfunction <- splinefun(x, y, method="natural")
res <- integrate(myfunction, lower=from, upper=to, subdivisions=subdivisions)$value
}
res
}
# library(microbenchmark)
#
# baseMode <- function(x, narm = FALSE) {
# if (narm) x <- x[!is.na(x)]
# ux <- unique(x)
# ux[which.max(table(match(x, ux)))]
# }
# x <- round(rnorm(1e7) *100, 4)
# microbenchmark(Mode(x), baseMode(x), DescTools:::fastMode(x), times = 15, unit = "relative")
#
# mode value, the most frequent element
Mode <- function(x, na.rm=FALSE) {
# // Source
# // https://stackoverflow.com/questions/55212746/rcpp-fast-statistical-mode-function-with-vector-input-of-any-type
# // Author: Ralf Stubner, Joseph Wood
if(!is.atomic(x) | is.matrix(x)) stop("Mode supports only atomic vectors. Use sapply(*, Mode) instead.")
if (na.rm)
x <- x[!is.na(x)]
if (anyNA(x))
# there are NAs, so no mode exist nor frequency
return(structure(NA_real_, freq = NA_integer_))
if(length(x) == 1L)
# only one value in x, x is the mode
# return(structure(x, freq = 1L))
# changed to: only one value in x, no mode defined
return(structure(NA_real_, freq = NA_integer_))
# we don't have NAs so far, either there were then we've already stopped
# or they've been stripped above
res <- fastModeX(x, narm=FALSE)
# no mode existing, if max freq is only 1 observation
if(length(res)== 0L & attr(res, "freq")==1L)
return(structure(NA_real_, freq = NA_integer_))
else
# order results kills the attribute
return(structure(res[order(res)], freq = attr(res, "freq")))
}
Gmean <- function (x, method = c("classic", "boot"),
conf.level = NA, sides = c("two.sided","left","right"),
na.rm = FALSE, ...) {
# see also: http://www.stata.com/manuals13/rameans.pdf
if(na.rm) x <- na.omit(x)
if(any(is.na(x) | (is_neg <- x < 0))){
if(any(na.omit(is_neg)))
warning("x contains negative values")
if(is.na(conf.level))
NA
else
c(NA, NA, NA)
} else if(any(x==0)) {
if(is.na(conf.level))
0
else
c(0, NA, NA)
} else {
if(is.na(conf.level))
exp(mean(log(x)))
else
exp(MeanCI(x=log(x), method = method,
conf.level = conf.level, sides = sides, ...))
}
}
Gsd <- function (x, na.rm = FALSE) {
if(na.rm) x <- na.omit(x)
is.na(x) <- x <= 0
exp(sd(log(x)))
}
Hmean <- function(x, method = c("classic", "boot"),
conf.level = NA, sides = c("two.sided","left","right"),
na.rm = FALSE, ...) {
# see also for alternative ci
# https://www.unistat.com/guide/confidence-intervals/
is.na(x) <- x <= 0
if(is.na(conf.level))
res <- 1 / mean(1/x, na.rm = na.rm)
else {
# res <- (1 / MeanCI(x = 1/x, method = method,
# conf.level = conf.level, sides = sides, na.rm=na.rm, ...))
#
# if(!is.na(conf.level)){
# res[2:3] <- c(min(res[2:3]), max(res[2:3]))
# if(res[2] < 0)
# res[c(2,3)] <- NA
# }
#
sides <- match.arg(sides, choices = c("two.sided", "left",
"right"), several.ok = FALSE)
if (sides != "two.sided")
conf.level <- 1 - 2 * (1 - conf.level)
res <- (1/(mci <- MeanCI(x = 1/x, method = method, conf.level = conf.level,
sides = "two.sided", na.rm = na.rm, ...)))[c(1, 3, 2)]
# check if lower ci < 0, if so return NA, as CI not defined see Stata definition
if( mci[2] <= 0)
res[2:3] <- NA
names(res) <- names(res)[c(1,3,2)]
if (sides == "left")
res[3] <- Inf
else if (sides == "right")
# it's not clear, if we should not set this to 0
res[2] <- NA
}
return(res)
}
TukeyBiweight <- function(x, const=9, na.rm = FALSE, conf.level = NA, ci.type = "bca", R=1000, ...) {
if(na.rm) x <- na.omit(x)
if(anyNA(x)) return(NA)
if(is.na(conf.level)){
# .Call("tbrm", as.double(x[!is.na(x)]), const)
res <- .Call("tbrm", PACKAGE="DescTools", as.double(x), const)
} else {
# adjusted bootstrap percentile (BCa) interval
boot.tbw <- boot(x, function(x, d) .Call("tbrm", PACKAGE="DescTools", as.double(x[d]), const), R=R, ...)
ci <- boot.ci(boot.tbw, conf=conf.level, type=ci.type)
res <- c(tbw=boot.tbw$t0, lwr.ci=ci[[4]][4], upr.ci=ci[[4]][5])
}
return(res)
}
## Originally from /u/ftp/NDK/Source-NDK-9/R/rg2-fkt.R :
.tauHuber <- function(x, mu, k=1.345, s = mad(x), resid = (x - mu)/s) {
## Purpose: Korrekturfaktor Tau fuer die Varianz von Huber-M-Schaetzern
## -------------------------------------------------------------------------
## Arguments: x = Daten mu = Lokations-Punkt k = Parameter der Huber Psi-Funktion
## -------------------------------------------------------------------------
## Author: Rene Locher Update: R. Frisullo 23.4.02; M.Maechler (as.log(); s, resid)
inr <- abs(resid) <= k
psi <- ifelse(inr, resid, sign(resid)*k) # psi (x)
psiP <- as.logical(inr)# = ifelse(abs(resid) <= k, 1, 0) # psi'(x)
length(x) * sum(psi^2) / sum(psiP)^2
}
.wgt.himedian <- function(x, weights = rep(1,n)) {
# Purpose: weighted hiMedian of x
# Author: Martin Maechler, Date: 14 Mar 2002
n <- length(x <- as.double(x))
stopifnot(storage.mode(weights) %in% c("integer", "double"))
if(n != length(weights))
stop("'weights' must have same length as 'x'")
# if(is.integer(weights)) message("using integer weights")
# Original
# .C(if(is.integer(weights)) "wgt_himed_i" else "wgt_himed",
# x, n, weights,
# res = double(1))$res
if(is.integer(weights))
.C("wgt_himed_i",
x, n, weights,
res = double(1))$res
else
.C("wgt_himed",
x, n, weights,
res = double(1))$res
}
## A modified "safe" (and more general) Huber estimator:
.huberM <-
function(x, k = 1.345, weights = NULL,
tol = 1e-06,
mu = if(is.null(weights)) median(x) else .wgt.himedian(x, weights),
s = if(is.null(weights)) mad(x, center=mu)
else .wgt.himedian(abs(x - mu), weights),
se = FALSE,
warn0scale = getOption("verbose"))
{
## Author: Martin Maechler, Date: 6 Jan 2003, ff
## implicit 'na.rm = TRUE':
if(any(i <- is.na(x))) {
x <- x[!i]
if(!is.null(weights)) weights <- weights[!i]
}
n <- length(x)
sum.w <-
if(!is.null(weights)) {
stopifnot(is.numeric(weights), weights >= 0, length(weights) == n)
sum(weights)
} else n
it <- 0L
NA. <- NA_real_
if(sum.w == 0) # e.g 'x' was all NA
return(list(mu = NA., s = NA., it = it, se = NA.)) # instead of error
if(se && !is.null(weights))
stop("Std.error computation not yet available for the case of 'weights'")
if (s <= 0) {
if(s < 0) stop("negative scale 's'")
if(warn0scale && n > 1)
warning("scale 's' is zero -- returning initial 'mu'")
}
else {
wsum <- if(is.null(weights)) sum else function(u) sum(u * weights)
repeat {
it <- it + 1L
y <- pmin(pmax(mu - k * s, x), mu + k * s)
mu1 <- wsum(y) / sum.w
if (abs(mu - mu1) < tol * s)
break
mu <- mu1
}
}
list(mu = mu, s = s, it = it,
SE = if(se) s * sqrt(.tauHuber(x, mu=mu, s=s, k=k) / n) else NA.)
}
HuberM <- function(x, k = 1.345, mu = median(x), s = mad(x, center=mu),
na.rm = FALSE, conf.level = NA, ci.type = c("wald", "boot"), ...){
# new interface to HuberM, making it less complex
# refer to robustbase::huberM if more control is required
if(na.rm) x <- na.omit(x)
if(anyNA(x)) return(NA)
if(is.na(conf.level)){
res <- .huberM(x=x, k=k, mu=mu, s=s, warn0scale=TRUE)$mu
return(res)
} else {
switch(match.arg(ci.type)
,"wald"={
res <- .huberM(x=x, k=k, mu=mu, s=s, se=TRUE, warn0scale=TRUE)
# Solution: (12.6.06) - Robuste Regression (Rg-2d) - Musterloeungen zu Serie 1
# r.loc$mu + c(-1,1)*qt(0.975,8)*sqrt(t.tau/length(d.ertrag))*r.loc$s
#
# Ruckstuhl's Loesung:
# (Sleep.HM$mu + c(-1,1)*qt(0.975, length(Sleep)-1) *
# sqrt(f.tau(Sleep, Sleep.HM$mu)) * Sleep.HM$s/sqrt(length(Sleep)))
# ci <- qnorm(1-(1-conf.level)/2) * res$SE
ci <- qt(1-(1-conf.level)/2, length(x)-1) *
sqrt(.tauHuber(x, res$mu, k=k)) * res$s/sqrt(length(x))
res <- c(hm=res$mu, lwr.ci=res$mu - ci, upr.ci=res$mu + ci)
}
,"boot" ={
R <- InDots(..., arg="R", default=1000)
bci.type <- InDots(..., arg="type", default="perc")
boot.hm <- boot(x, function(x, d){
hm <- .huberM(x=x[d], k=k, mu=mu, s=s, se=TRUE)
return(c(hm$mu, hm$s^2))
}, R=R)
ci <- boot.ci(boot.hm, conf=conf.level, ...)
if(ci.type =="norm") {
lwr.ci <- ci[[4]][2]
upr.ci <- ci[[4]][3]
} else {
lwr.ci <- ci[[4]][4]
upr.ci <- ci[[4]][5]
}
res <- c(hm=boot.hm$t0[1], lwr.ci=lwr.ci, upr.ci=upr.ci)
}
)
return(res)
}
}
# old version, replace 13.5.2015
#
# # A modified "safe" (and more general) Huber estimator:
# HuberM <- function(x, k = 1.5, weights = NULL, tol = 1e-06,
# mu = if(is.null(weights)) median(x) else wgt.himedian(x, weights),
# s = if(is.null(weights)) mad(x, center=mu) else wgt.himedian(abs(x - mu), weights),
# se = FALSE, warn0scale = getOption("verbose"), na.rm = FALSE, stats = FALSE) {
#
# # Author: Martin Maechler, Date: 6 Jan 2003, ff
#
# # Originally from /u/ftp/NDK/Source-NDK-9/R/rg2-fkt.R :
# tauHuber <- function(x, mu, k=1.5, s = mad(x), resid = (x - mu)/s) {
# # Purpose: Korrekturfaktor Tau fuer die Varianz von Huber-M-Schaetzern
# # ******************************************************************************
# # Arguments: x = Daten mu = Lokations-Punkt k = Parameter der Huber Psi-Funktion
# # ******************************************************************************
# # Author: Rene Locher Update: R. Frisullo 23.4.02; M.Maechler (as.log(); s, resid)
# inr <- abs(resid) <= k
# psi <- ifelse(inr, resid, sign(resid)*k) #### psi (x)
# psiP <- as.logical(inr) # = ifelse(abs(resid) <= k, 1, 0) #### psi'(x)
# length(x) * sum(psi^2) / sum(psiP)^2
# }
#
# wgt.himedian <- function(x, weights = rep(1,n)) {
#
# # Purpose: weighted hiMedian of x
# # Author: Martin Maechler, Date: 14 Mar 2002
# n <- length(x <- as.double(x))
# stopifnot(storage.mode(weights) %in% c("integer", "double"))
# if(n != length(weights))
# stop("'weights' must have same length as 'x'")
# # if(is.integer(weights)) message("using integer weights")
# .C(if(is.integer(weights)) "wgt_himed_i" else "wgt_himed",
# x, n, weights,
# res = double(1))$res
# }
#
#
# # Andri: introduce na.rm
# # old: implicit 'na.rm = TRUE'
# if(na.rm) {
# i <- is.na(x)
# x <- x[!i]
# if(!is.null(weights)) weights <- weights[!i]
# } else {
# if(anyNA(x)) return(NA)
# }
#
#
# n <- length(x)
# sum.w <-
# if(!is.null(weights)) {
# stopifnot(is.numeric(weights), weights >= 0, length(weights) == n)
# sum(weights)
# } else n
# it <- 0L
# NA. <- NA_real_
# if(sum.w == 0) # e.g 'x' was all NA
# return(list(mu = NA., s = NA., it = it, se = NA.)) # instead of error
#
# if(se && !is.null(weights))
# stop("Std.error computation not yet available for the case of 'weights'")
#
# if (s <= 0) {
# if(s < 0) stop("negative scale 's'")
# if(warn0scale && n > 1)
# warning("scale 's' is zero -- returning initial 'mu'")
# }
# else {
# wsum <- if(is.null(weights)) sum else function(u) sum(u * weights)
#
# repeat {
# it <- it + 1L
# y <- pmin(pmax(mu - k * s, x), mu + k * s)
# mu1 <- wsum(y) / sum.w
# if (abs(mu - mu1) < tol * s)
# break
# mu <- mu1
# }
# }
#
# if(stats)
# res <- list(mu = mu, s = s, it = it,
# SE = if(se) s * sqrt(tauHuber(x, mu=mu, s=s, k=k) / n) else NA.)
# else
# res <- mu
#
# return(res)
#
# }
HodgesLehmann <- function(x, y = NULL, conf.level = NA, na.rm = FALSE) {
# Werner Stahel's version:
#
# f.HodgesLehmann <- function(data)
# {
# ## Purpose: Hodges-Lehmann estimate and confidence interval
# ## -------------------------------------------------------------------------
# ## Arguments:
# ## Remark: function changed so that CI covers >= 95%, before it was too
# ## small (9/22/04)
# ## -------------------------------------------------------------------------
# ## Author: Werner Stahel, Date: 12 Aug 2002, 14:13
# ## Update: Beat Jaggi, Date: 22 Sept 2004
# .cexact <-
# # c(NA,NA,NA,NA,NA,21,26,33,40,47,56,65,74,84,95,107,119,131,144,158)
# c(NA,NA,NA,NA,NA,22,27,34,41,48,57,66,75,85,96,108,120,132,145,159)
# .d <- na.omit(data)
# .n <- length(.d)
# .wa <- sort(c(outer(.d,.d,"+")/2)[outer(1:.n,1:.n,"<=")])
# .c <- if (.n<=length(.cexact)) .n*(.n+1)/2+1-.cexact[.n] else
# floor(.n*(.n+1)/4-1.96*sqrt(.n*(.n+1)*(2*.n+1)/24))
# .r <- c(median(.wa), .wa[c(.c,.n*(.n+1)/2+1-.c)])
# names(.r) <- c("estimate","lower","upper")
# .r
# }
if(na.rm) {
if(is.null(y))
x <- na.omit(x)
else {
ok <- complete.cases(x, y)
x <- x[ok]
y <- y[ok]
}
}
if(anyNA(x) || (!is.null(y) && anyNA(y)))
if(is.na(conf.level))
return(NA)
else
return(c(est=NA, lwr.ci=NA, upr.ci=NA))
# res <- wilcox.test(x, y, conf.int = TRUE, conf.level = Coalesce(conf.level, 0.8))
if(is.null(y)){
res <- .Call("_DescTools_hlqest", PACKAGE = "DescTools", x)
} else {
res <- .Call("_DescTools_hl2qest", PACKAGE = "DescTools", x, y)
}
if(is.na(conf.level)){
result <- res
names(result) <- NULL
} else {
n <- length(x)
# lci <- n^2/2 + qnorm((1-conf.level)/2) * sqrt(n^2 * (2*n+1)/12) - 0.5
# uci <- n^2/2 - qnorm((1-conf.level)/2) * sqrt(n^2 * (2*n+1)/12) - 0.5
lci <- uci <- NA
warning("Confidence intervals not yet implemented for Hodges-Lehman-Estimator.")
result <- c(est=res, lwr.ci=lci, upr.ci=uci)
}
return(result)
}
Skew <- function (x, weights=NULL, na.rm = FALSE, method = 3, conf.level = NA, ci.type = "bca", R=1000, ...) {
# C part for the expensive (x - mean(x))^2 etc. is a kind of 14 times faster
# > x <- rchisq(100000000, df=2)
# > system.time(Skew(x))
# user system elapsed
# 6.32 0.30 6.62
# > system.time(Skew2(x))
# user system elapsed
# 0.47 0.00 0.47
i.skew <- function(x, weights=NULL, method = 3) {
# method 1: older textbooks
if(!is.null(weights)){
# use a standard treatment for weights
z <- .NormWeights(x, weights, na.rm=na.rm, zero.rm=TRUE)
r.skew <- .Call("rskeww",
as.numeric(z$x), as.numeric(Mean(z$x, weights = z$weights)),
as.numeric(z$weights), PACKAGE="DescTools")
n <- z$wsum
} else {
if (na.rm) x <- na.omit(x)
r.skew <- .Call("rskew", as.numeric(x),
as.numeric(mean(x)), PACKAGE="DescTools")
n <- length(x)
}
se <- sqrt((6*(n-2))/((n+1)*(n+3)))
if (method == 2) {
# method 2: SAS/SPSS
r.skew <- r.skew * n^0.5 * (n - 1)^0.5/(n - 2)
se <- se * sqrt(n*(n-1))/(n-2)
}
else if (method == 3) {
# method 3: MINITAB/BDMP
r.skew <- r.skew * ((n - 1)/n)^(3/2)
se <- se * ((n - 1)/n)^(3/2)
}
return(c(r.skew, se^2))
}
if(is.na(conf.level)){
res <- i.skew(x, weights=weights, method=method)[1]
} else {
if(ci.type == "classic") {
res <- i.skew(x, weights=weights, method=method)
res <- c(skewness=res[1],
lwr.ci=qnorm((1-conf.level)/2) * sqrt(res[2]),
upr.ci=qnorm(1-(1-conf.level)/2) * sqrt(res[2]))
} else {
# Problematic standard errors and confidence intervals for skewness and kurtosis.
# Wright DB, Herrington JA. (2011) recommend only bootstrap intervals
# adjusted bootstrap percentile (BCa) interval
boot.skew <- boot(x, function(x, d) i.skew(x[d], weights=weights, method=method), R=R, ...)
ci <- boot.ci(boot.skew, conf=conf.level, type=ci.type)
if(ci.type =="norm") {
lwr.ci <- ci[[4]][2]
upr.ci <- ci[[4]][3]
} else {
lwr.ci <- ci[[4]][4]
upr.ci <- ci[[4]][5]
}
res <- c(skew=boot.skew$t0[1], lwr.ci=lwr.ci, upr.ci=upr.ci)
}
}
return(res)
}
Kurt <- function (x, weights=NULL, na.rm = FALSE, method = 3, conf.level = NA,
ci.type = "bca", R=1000, ...) {
i.kurt <- function(x, weights=NULL, na.rm = FALSE, method = 3) {
# method 1: older textbooks
if(!is.null(weights)){
# use a standard treatment for weights
z <- .NormWeights(x, weights, na.rm=na.rm, zero.rm=TRUE)
r.kurt <- .Call("rkurtw", as.numeric(z$x), as.numeric(Mean(z$x, weights = z$weights)), as.numeric(z$weights), PACKAGE="DescTools")
n <- z$wsum
} else {
if (na.rm) x <- na.omit(x)
r.kurt <- .Call("rkurt", as.numeric(x), as.numeric(mean(x)), PACKAGE="DescTools")
n <- length(x)
}
se <- sqrt((24*n*(n-2)*(n-3))/((n+1)^2*(n+3)*(n+5)))
if (method == 2) {
# method 2: SAS/SPSS
r.kurt <- ((r.kurt + 3) * (n + 1)/(n - 1) - 3) * (n - 1)^2/(n - 2)/(n - 3)
se <- se * (((n-1)*(n+1))/((n-2)*(n-3)))
}
else if (method == 3) {
# method 3: MINITAB/BDMP
r.kurt <- (r.kurt + 3) * (1 - 1/n)^2 - 3
se <- se * ((n-1)/n)^2
}
return(c(r.kurt, se^2))
}
if(is.na(conf.level)){
res <- i.kurt(x, weights=weights, na.rm=na.rm, method=method)[1]
} else {
if(ci.type == "classic") {
res <- i.kurt(x, weights=weights, method=method)
res <- c(kurtosis=res[1],
lwr.ci=qnorm((1-conf.level)/2) * sqrt(res[2]),
upr.ci=qnorm(1-(1-conf.level)/2) * sqrt(res[2]))
} else {
# Problematic standard errors and confidence intervals for skewness and kurtosis.
# Wright DB, Herrington JA. (2011) recommend only bootstrap intervals
# adjusted bootstrap percentile (BCa) interval
boot.kurt <- boot(x, function(x, d) i.kurt(x[d], weights=weights, na.rm=na.rm, method=method), R=R, ...)
ci <- boot.ci(boot.kurt, conf=conf.level, type=ci.type)
if(ci.type =="norm") {
lwr.ci <- ci[[4]][2]
upr.ci <- ci[[4]][3]
} else {
lwr.ci <- ci[[4]][4]
upr.ci <- ci[[4]][5]
}
res <- c(kurt=boot.kurt$t0[1], lwr.ci=lwr.ci, upr.ci=upr.ci)
}
}
return(res)
}
Outlier <- function(x, method=c("boxplot", "hampel"), value=TRUE, na.rm=FALSE){
switch(match.arg(arg = method, choices = c("boxplot", "hampel")),
# boxplot = { x[x %)(% (quantile(x, c(0.25,0.75), na.rm=na.rm) + c(-1,1) * 1.5*IQR(x,na.rm=na.rm))] }
boxplot = {
# old, replaced by v. 0.99.26
# res <- boxplot(x, plot = FALSE)$out
qq <- quantile(as.numeric(x), c(0.25, 0.75), na.rm = na.rm, names = FALSE)
iqr <- diff(qq)
id <- x < (qq[1] - 1.5 * iqr) | x > (qq[2] + 1.5 * iqr)
},
hampel = {
# hampel considers values outside of median ± 3*(median absolute deviation) to be outliers
id <- x %][% (median(x, na.rm=na.rm) + 3 * c(-1, 1) * mad(x, na.rm=na.rm))
}
)
if(value)
res <- x[id]
else
res <- which(id)
res <- res[!is.na(res)]
return(res)
}
LOF <- function(data,k) {
# source: library(dprep)
# A function that finds the local outlier factor (Breunig,2000) of
# the matrix "data" with k neighbors
# Adapted by Caroline Rodriguez and Edgar Acuna, may 2004
knneigh.vect <-
function(x,data,k)
{
#Function that returns the distance from a vector "x" to
#its k-nearest-neighbors in the matrix "data"
temp=as.matrix(data)
numrow=dim(data)[1]
dimnames(temp)=NULL
#subtract rowvector x from each row of data
difference<- scale(temp, x, FALSE)
#square and add all differences and then take the square root
dtemp <- drop(difference^2 %*% rep(1, ncol(data)))
dtemp=sqrt(dtemp)
#order the distances
order.dist <- order(dtemp)
nndist=dtemp[order.dist]
#find distance to k-nearest neighbor
#uses k+1 since first distance in vector is a 0
knndist=nndist[k+1]
#find neighborhood
#eliminate first row of zeros from neighborhood
neighborhood=drop(nndist[nndist<=knndist])
neighborhood=neighborhood[-1]
numneigh=length(neighborhood)
#find indexes of each neighbor in the neighborhood
index.neigh=order.dist[1:numneigh+1]
# this will become the index of the distance to first neighbor
num1=length(index.neigh)+3
# this will become the index of the distance to last neighbor
num2=length(index.neigh)+numneigh+2
#form a vector
neigh.dist=c(num1,num2,index.neigh,neighborhood)
return(neigh.dist)
}
dist.to.knn <-
function(dataset,neighbors)
{
#function returns an object in which each column contains
#the indices of the first k neighbors followed by the
#distances to each of these neighbors
numrow=dim(dataset)[1]
#applies a function to find distance to k nearest neighbors
#within "dataset" for each row of the matrix "dataset"
knndist=rep(0,0)
for (i in 1:numrow)
{
#find obervations that make up the k-distance neighborhood for dataset[i,]
neighdist=knneigh.vect(dataset[i,],dataset,neighbors)
#adjust the length of neighdist or knndist as needed to form matrix of neighbors
#and their distances
if (i==2)
{
if (length(knndist)<length(neighdist))
{
z=length(neighdist)-length(knndist)
zeros=rep(0,z)
knndist=c(knndist,zeros)
}
else if (length(knndist)>length(neighdist))
{
z=length(knndist)-length(neighdist)
zeros=rep(0,z)
neighdist=c(neighdist,zeros)
}
}
else
{
if (i!=1)
{
if (dim(knndist)[1]<length(neighdist))
{
z=(length(neighdist)-dim(knndist)[1])
zeros=rep(0,z*dim(knndist)[2])
zeros=matrix(zeros,z,dim(knndist)[2])
knndist=rbind(knndist,zeros)
}
else if (dim(knndist)[1]>length(neighdist))
{
z=(dim(knndist)[1]-length(neighdist))
zeros=rep(0,z)
neighdist=c(neighdist,zeros)
}
}
}
knndist=cbind(knndist,neighdist)
}
return(knndist)
}
reachability <-
function(distdata,k)
{
# function that calculates the local reachability density
# of Breuing(2000) for each observation in a matrix, using
# a matrix (distdata) of k nearest neighbors computed by the function dist.to.knn2
p=dim(distdata)[2]
lrd=rep(0,p)
for (i in 1:p)
{
j=seq(3,3+(distdata[2,i]-distdata[1,i]))
# compare the k-distance from each observation to its kth neighbor
# to the actual distance between each observation and its neighbors
numneigh=distdata[2,i]-distdata[1,i]+1
temp=rbind(diag(distdata[distdata[2,distdata[j,i]],distdata[j,i]]),distdata[j+numneigh,i])
# calculate reachability
reach=1/(sum(apply(temp,2,max))/numneigh)
lrd[i]=reach
}
lrd
}
data=as.matrix(data)
# find k nearest neighbors and their distance from each observation
# in data
distdata=dist.to.knn(data,k)
p=dim(distdata)[2]
# calculate the local reachability density for each observation in data
lrddata=reachability(distdata,k)
lof=rep(0,p)
# computer the local outlier factor of each observation in data
for ( i in 1:p)
{
nneigh=distdata[2,i]-distdata[1,i]+1
j=seq(0,(nneigh-1))
local.factor=sum(lrddata[distdata[3+j,i]]/lrddata[i])/nneigh
lof[i]=local.factor
}
# return lof, a vector with the local outlier factor of each observation
lof
}
BootCI <- function(x, y=NULL, FUN, ..., bci.method = c("norm", "basic", "stud", "perc", "bca"),
conf.level = 0.95, sides = c("two.sided", "left", "right"), R = 999) {
dots <- as.list(substitute(list( ... ))) [-1]
bci.method <- match.arg(bci.method)
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
if(is.null(y))
boot.fun <- boot(x, function(x, d) do.call(FUN, append(list(x[d]), dots)), R = R)
else
boot.fun <- boot(x, function(x, d) do.call(FUN, append(list(x[d], y[d]), dots)), R = R)
ci <- boot.ci(boot.fun, conf=conf.level, type=bci.method)
if (bci.method == "norm") {
res <- c(est = boot.fun$t0, lwr.ci = ci[[4]][2],
upr.ci = ci[[4]][3])
} else {
res <- c(est = boot.fun$t0, lwr.ci = ci[[4]][4],
upr.ci = ci[[4]][5])
}
if(sides=="left")
res[3] <- Inf
else if(sides=="right")
res[2] <- -Inf
names(res)[1] <- deparse(substitute(FUN))
return(res)
}
# Confidence Intervals for Binomial Proportions
BinomCI <- function(x, n, conf.level = 0.95, sides = c("two.sided","left","right"),
method = c("wilson", "wald", "waldcc", "agresti-coull", "jeffreys", "modified wilson", "wilsoncc",
"modified jeffreys", "clopper-pearson", "arcsine", "logit", "witting", "pratt", "midp", "lik", "blaker"),
rand = 123, tol=1e-05, std_est=TRUE) {
if(missing(method)) method <- "wilson"
if(missing(sides)) sides <- "two.sided"
iBinomCI <- function(x, n, conf.level = 0.95, sides = c("two.sided","left","right"),
method = c("wilson", "wilsoncc", "wald", "waldcc","agresti-coull", "jeffreys", "modified wilson",
"modified jeffreys", "clopper-pearson", "arcsine", "logit", "witting", "pratt", "midp", "lik", "blaker"),
rand = 123, tol=1e-05, std_est=TRUE) {
if(length(x) != 1) stop("'x' has to be of length 1 (number of successes)")
if(length(n) != 1) stop("'n' has to be of length 1 (number of trials)")
if(length(conf.level) != 1) stop("'conf.level' has to be of length 1 (confidence level)")
if(conf.level < 0.5 | conf.level > 1) stop("'conf.level' has to be in [0.5, 1]")
method <- match.arg(arg=method,
choices=c("wilson", "wald", "waldcc", "wilsoncc","agresti-coull",
"jeffreys", "modified wilson",
"modified jeffreys", "clopper-pearson", "arcsine",
"logit", "witting","pratt", "midp", "lik", "blaker"))
sides <- match.arg(sides, choices = c("two.sided","left","right"),
several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
alpha <- 1 - conf.level
kappa <- qnorm(1-alpha/2)
p.hat <- x/n
q.hat <- 1 - p.hat
# this is the default estimator used by the most (but not all) methods
est <- p.hat
switch( method
, "wald" = {
term2 <- kappa*sqrt(p.hat*q.hat)/sqrt(n)
CI.lower <- max(0, p.hat - term2)
CI.upper <- min(1, p.hat + term2)
}
, "waldcc" = {
term2 <- kappa*sqrt(p.hat*q.hat)/sqrt(n)
# continuity correction
term2 <- term2 + 1/(2*n)
CI.lower <- max(0, p.hat - term2)
CI.upper <- min(1, p.hat + term2)
}
, "wilson" = {
# non standard estimator
if(!std_est){
x.tilde <- x + kappa^2/2
n.tilde <- n + kappa^2
p.tilde <- x.tilde/n.tilde
est <- p.tilde
}
term1 <- (x + kappa^2/2)/(n + kappa^2)
term2 <- kappa*sqrt(n)/(n + kappa^2)*sqrt(p.hat*q.hat + kappa^2/(4*n))
CI.lower <- max(0, term1 - term2)
CI.upper <- min(1, term1 + term2)
}
, "wilsoncc" = {
# non standard estimator
if(!std_est){
x.tilde <- x + kappa^2/2
n.tilde <- n + kappa^2
p.tilde <- x.tilde/n.tilde
est <- p.tilde
}
lci <- ( 2*x+kappa**2 -1 - kappa*sqrt(kappa**2 -
2- 1/n + 4*p.hat*(n*q.hat+1))) / (2*(n+kappa**2))
uci <- ( 2*x+kappa**2 +1 + kappa*sqrt(kappa**2 +
2- 1/n + 4*p.hat*(n*q.hat-1))) / (2*(n+kappa**2))
CI.lower <- max(0, ifelse(p.hat==0, 0, lci))
CI.upper <- min(1, ifelse(p.hat==1, 1, uci))
}
, "agresti-coull" = {
x.tilde <- x + kappa^2/2
n.tilde <- n + kappa^2
p.tilde <- x.tilde/n.tilde
q.tilde <- 1 - p.tilde
# non standard estimator
if(!std_est)
est <- p.tilde
term2 <- kappa*sqrt(p.tilde*q.tilde)/sqrt(n.tilde)
CI.lower <- max(0, p.tilde - term2)
CI.upper <- min(1, p.tilde + term2)
}
, "jeffreys" = {
if(x == 0)
CI.lower <- 0
else
CI.lower <- qbeta(alpha/2, x+0.5, n-x+0.5)
if(x == n)
CI.upper <- 1
else
CI.upper <- qbeta(1-alpha/2, x+0.5, n-x+0.5)
}
, "modified wilson" = {
# non standard estimator
if(!std_est){
x.tilde <- x + kappa^2/2
n.tilde <- n + kappa^2
p.tilde <- x.tilde/n.tilde
est <- p.tilde
}
term1 <- (x + kappa^2/2)/(n + kappa^2)
term2 <- kappa*sqrt(n)/(n + kappa^2)*sqrt(p.hat*q.hat + kappa^2/(4*n))
## comment by Andre Gillibert, 19.6.2017:
## old:
## if((n <= 50 & x %in% c(1, 2)) | (n >= 51 & n <= 100 & x %in% c(1:3)))
## new:
if((n <= 50 & x %in% c(1, 2)) | (n >= 51 & x %in% c(1:3)))
CI.lower <- 0.5*qchisq(alpha, 2*x)/n
else
CI.lower <- max(0, term1 - term2)
## if((n <= 50 & x %in% c(n-1, n-2)) | (n >= 51 & n <= 100 & x %in% c(n-(1:3))))
if((n <= 50 & x %in% c(n-1, n-2)) | (n >= 51 & x %in% c(n-(1:3))))
CI.upper <- 1 - 0.5*qchisq(alpha, 2*(n-x))/n
else
CI.upper <- min(1, term1 + term2)
}
, "modified jeffreys" = {
if(x == n)
CI.lower <- (alpha/2)^(1/n)
else {
if(x <= 1)
CI.lower <- 0
else
CI.lower <- qbeta(alpha/2, x+0.5, n-x+0.5)
}
if(x == 0)
CI.upper <- 1 - (alpha/2)^(1/n)
else{
if(x >= n-1)
CI.upper <- 1
else
CI.upper <- qbeta(1-alpha/2, x+0.5, n-x+0.5)
}
}
, "clopper-pearson" = {
CI.lower <- qbeta(alpha/2, x, n-x+1)
CI.upper <- qbeta(1-alpha/2, x+1, n-x)
}
, "arcsine" = {
p.tilde <- (x + 0.375)/(n + 0.75)
# non standard estimator
if(!std_est)
est <- p.tilde
CI.lower <- sin(asin(sqrt(p.tilde)) - 0.5*kappa/sqrt(n))^2
CI.upper <- sin(asin(sqrt(p.tilde)) + 0.5*kappa/sqrt(n))^2
}
, "logit" = {
lambda.hat <- log(x/(n-x))
V.hat <- n/(x*(n-x))
lambda.lower <- lambda.hat - kappa*sqrt(V.hat)
lambda.upper <- lambda.hat + kappa*sqrt(V.hat)
CI.lower <- exp(lambda.lower)/(1 + exp(lambda.lower))
CI.upper <- exp(lambda.upper)/(1 + exp(lambda.upper))
}
, "witting" = {
set.seed(rand)
x.tilde <- x + runif(1, min = 0, max = 1)
pbinom.abscont <- function(q, size, prob){
v <- trunc(q)
term1 <- pbinom(v-1, size = size, prob = prob)
term2 <- (q - v)*dbinom(v, size = size, prob = prob)
return(term1 + term2)
}
qbinom.abscont <- function(p, size, x){
fun <- function(prob, size, x, p){
pbinom.abscont(x, size, prob) - p
}
uniroot(fun, interval = c(0, 1), size = size, x = x, p = p)$root
}
CI.lower <- qbinom.abscont(1-alpha, size = n, x = x.tilde)
CI.upper <- qbinom.abscont(alpha, size = n, x = x.tilde)
}
, "pratt" = {
if(x==0) {
CI.lower <- 0
CI.upper <- 1-alpha^(1/n)
} else if(x==1) {
CI.lower <- 1-(1-alpha/2)^(1/n)
CI.upper <- 1-(alpha/2)^(1/n)
} else if(x==(n-1)) {
CI.lower <- (alpha/2)^(1/n)
CI.upper <- (1-alpha/2)^(1/n)
} else if(x==n) {
CI.lower <- alpha^(1/n)
CI.upper <- 1
} else {
z <- qnorm(1 - alpha/2)
A <- ((x+1) / (n-x))^2
B <- 81*(x+1)*(n-x)-9*n-8
C <- (0-3)*z*sqrt(9*(x+1)*(n-x)*(9*n+5-z^2)+n+1)
D <- 81*(x+1)^2-9*(x+1)*(2+z^2)+1
E <- 1+A*((B+C)/D)^3
CI.upper <- 1/E
A <- (x / (n-x-1))^2
B <- 81*x*(n-x-1)-9*n-8
C <- 3*z*sqrt(9*x*(n-x-1)*(9*n+5-z^2)+n+1)
D <- 81*x^2-9*x*(2+z^2)+1
E <- 1+A*((B+C)/D)^3
CI.lower <- 1/E
}
}
, "midp" = {
#Functions to find root of for the lower and higher bounds of the CI
#These are helper functions.
f.low <- function(pi, x, n) {
1/2*dbinom(x, size=n, prob=pi) +
pbinom(x, size=n, prob=pi, lower.tail=FALSE) - (1-conf.level)/2
}
f.up <- function(pi, x, n) {
1/2*dbinom(x, size=n, prob=pi) +
pbinom(x-1, size=n, prob=pi) - (1-conf.level)/2
}
# One takes pi_low = 0 when x=0 and pi_up=1 when x=n
CI.lower <- 0
CI.upper <- 1
# Calculate CI by finding roots of the f funcs
if (x!=0) {
CI.lower <- uniroot(f.low, interval=c(0, p.hat), x=x, n=n)$root
}
if (x!=n) {
CI.upper <- uniroot(f.up, interval=c(p.hat, 1), x=x, n=n)$root
}
}
, "lik" = {
CI.lower <- 0
CI.upper <- 1
z <- qnorm(1 - alpha * 0.5)
# preset tolerance, should we offer function argument?
tol = .Machine$double.eps^0.5
BinDev <- function(y, x, mu, wt, bound = 0,
tol = .Machine$double.eps^0.5, ...) {
# returns the binomial deviance for y, x, wt
ll.y <- ifelse(y %in% c(0, 1), 0, dbinom(x, wt, y, log=TRUE))
ll.mu <- ifelse(mu %in% c(0, 1), 0, dbinom(x, wt, mu, log=TRUE))
res <- ifelse(abs(y - mu) < tol, 0,
sign(y - mu) * sqrt(-2 * (ll.y - ll.mu)))
return(res - bound)
}
if(x!=0 && tol<p.hat) {
CI.lower <- if(BinDev(tol, x, p.hat, n, -z, tol) <= 0) {
uniroot(f = BinDev, interval = c(tol, if(p.hat < tol || p.hat == 1) 1 - tol else p.hat),
bound = -z, x = x, mu = p.hat, wt = n)$root }
}
if(x!=n && p.hat<(1-tol)) {
CI.upper <- if(BinDev(y = 1 - tol, x = x, mu = ifelse(p.hat > 1 - tol, tol, p.hat),
wt = n, bound = z, tol = tol) < 0) {
CI.lower <- if(BinDev(tol, x, if(p.hat < tol || p.hat == 1) 1 - tol else p.hat, n, -z, tol) <= 0) {
uniroot(f = BinDev, interval = c(tol, p.hat),
bound = -z, x = x, mu = p.hat, wt = n)$root }
} else {
uniroot(f = BinDev, interval = c(if(p.hat > 1 - tol) tol else p.hat, 1 - tol),
bound = z, x = x, mu = p.hat, wt = n)$root }
}
}
, "blaker" ={
acceptbin <- function (x, n, p) {
p1 <- 1 - pbinom(x - 1, n, p)
p2 <- pbinom(x, n, p)
a1 <- p1 + pbinom(qbinom(p1, n, p) - 1, n, p)
a2 <- p2 + 1 - pbinom(qbinom(1 - p2, n, p), n, p)
return(min(a1, a2))
}
CI.lower <- 0
CI.upper <- 1
if (x != 0) {
CI.lower <- qbeta((1 - conf.level)/2, x, n - x + 1)
while (acceptbin(x, n, CI.lower + tol) < (1 - conf.level))
CI.lower = CI.lower + tol
}
if (x != n) {
CI.upper <- qbeta(1 - (1 - conf.level)/2, x + 1, n - x)
while (acceptbin(x, n, CI.upper - tol) < (1 - conf.level))
CI.upper <- CI.upper - tol
}
}
)
# dot not return ci bounds outside [0,1]
ci <- c( est=est, lwr.ci=max(0, CI.lower), upr.ci=min(1, CI.upper) )
if(sides=="left")
ci[3] <- 1
else if(sides=="right")
ci[2] <- 0
return(ci)
}
# handle vectors
# which parameter has the highest dimension
lst <- list(x=x, n=n, conf.level=conf.level, sides=sides,
method=method, rand=rand, std_est=std_est)
maxdim <- max(unlist(lapply(lst, length)))
# recycle all params to maxdim
lgp <- lapply( lst, rep, length.out=maxdim )
# # increase conf.level for one sided intervals
# lgp$conf.level[lgp.sides!="two.sided"] <- 1 - 2*(1-lgp$conf.level[lgp.sides!="two.sided"])
# get rownames
lgn <- DescTools::Recycle(x=if(is.null(names(x))) paste("x", seq_along(x), sep=".") else names(x),
n=if(is.null(names(n))) paste("n", seq_along(n), sep=".") else names(n),
conf.level=conf.level, sides=sides, method=method, std_est=std_est)
xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) length(unique(x)) != 1)]), 1, paste, collapse=":")
res <- t(sapply(1:maxdim, function(i) iBinomCI(x=lgp$x[i], n=lgp$n[i],
conf.level=lgp$conf.level[i],
sides=lgp$sides[i],
method=lgp$method[i], rand=lgp$rand[i],
std_est=lgp$std_est[i])))
colnames(res)[1] <- c("est")
rownames(res) <- xn
# if(nrow(res)==1)
# res <- res[1,]
return(res)
}
BinomCIn <- function(p=0.5, width, interval=c(1, 1e5), conf.level=0.95, sides="two.sided", method="wilson") {
uniroot(f = function(n) diff(BinomCI(x=p*n, n=n, conf.level=conf.level,
sides=sides, method=method)[-1]) - width,
interval = interval)$root
}
BinomDiffCI <- function(x1, n1, x2, n2, conf.level = 0.95, sides = c("two.sided","left","right"),
method=c("ac", "wald", "waldcc", "score", "scorecc", "mn",
"mee", "blj", "ha", "hal", "jp")) {
if(missing(sides)) sides <- match.arg(sides)
if(missing(method)) method <- match.arg(method)
iBinomDiffCI <- function(x1, n1, x2, n2, conf.level, sides, method) {
# .Wald #1
# .Wald (Corrected) #2
# .Exact
# .Exact (FM Score)
# .Newcombe Score #10
# .Newcombe Score (Corrected) #11
# .Farrington-Manning
# .Hauck-Anderson
# http://www.jiangtanghu.com/blog/2012/09/23/statistical-notes-5-confidence-intervals-for-difference-between-independent-binomial-proportions-using-sas/
# Interval estimation for the difference between independent proportions: comparison of eleven methods.
# https://www.lexjansen.com/wuss/2016/127_Final_Paper_PDF.pdf
# http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.633.9380&rep=rep1&type=pdf
# Newcombe (1998) (free):
# http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.408.7354&rep=rep1&type=pdf
# no need to check args here, they're already ok...
# method <- match.arg(arg = method,
# choices = c("wald", "waldcc", "ac", "score", "scorecc", "mn",
# "mee", "blj", "ha", "hal", "jp"))
#
# sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
alpha <- 1 - conf.level
kappa <- qnorm(1 - alpha/2)
p1.hat <- x1/n1
p2.hat <- x2/n2
est <- p1.hat - p2.hat
switch(method,
"wald" = {
vd <- p1.hat*(1-p1.hat)/n1 + p2.hat*(1-p2.hat)/n2
term2 <- kappa * sqrt(vd)
CI.lower <- max(-1, est - term2)
CI.upper <- min(1, est + term2)
},
"waldcc" = {
vd <- p1.hat*(1-p1.hat)/n1 + p2.hat*(1-p2.hat)/n2
term2 <- kappa * sqrt(vd)
term2 <- term2 + 0.5 * (1/n1+1/n2)
CI.lower <- max(-1, est - term2)
CI.upper <- min(1, est + term2)
},
"ac" = { # Agresti-Caffo
n1 <- n1+2
n2 <- n2+2
x1 <- x1+1
x2 <- x2+1
p1.hat <- x1/n1
p2.hat <- x2/n2
est1 <- p1.hat - p2.hat
vd <- p1.hat*(1-p1.hat)/n1 + p2.hat*(1-p2.hat)/n2
term2 <- kappa * sqrt(vd)
CI.lower <- max(-1, est1 - term2)
CI.upper <- min(1, est1 + term2)
} ,
"exact" = { # exact
CI.lower <- NA
CI.upper <- NA
},
"score" = { # Newcombe
w1 <- BinomCI(x=x1, n=n1, conf.level=conf.level, method="wilson")
w2 <- BinomCI(x=x2, n=n2, conf.level=conf.level, method="wilson")
l1 <- w1[2]
u1 <- w1[3]
l2 <- w2[2]
u2 <- w2[3]
CI.lower <- est - kappa * sqrt(l1*(1-l1)/n1 + u2*(1-u2)/n2)
CI.upper <- est + kappa * sqrt(u1*(1-u1)/n1 + l2*(1-l2)/n2)
},
"scorecc" = { # Newcombe
w1 <- BinomCI(x=x1, n=n1, conf.level=conf.level, method="wilsoncc")
w2 <- BinomCI(x=x2, n=n2, conf.level=conf.level, method="wilsoncc")
l1 <- w1[2]
u1 <- w1[3]
l2 <- w2[2]
u2 <- w2[3]
CI.lower <- max(-1, est - sqrt((p1.hat - l1)^2 + (u2-p2.hat)^2) )
CI.upper <- min( 1, est + sqrt((u1-p1.hat)^2 + (p2.hat-l2)^2) )
},
"mee" = { # Mee, also called Farrington-Mannig
.score <- function (p1, n1, p2, n2, dif) {
if(dif > 1) dif <- 1
if(dif < -1) dif <- -1
diff <- p1 - p2 - dif
if (abs(diff) == 0) {
res <- 0
} else {
t <- n2/n1
a <- 1 + t
b <- -(1 + t + p1 + t * p2 + dif * (t + 2))
c <- dif * dif + dif * (2 * p1 + t + 1) + p1 + t * p2
d <- -p1 * dif * (1 + dif)
v <- (b/a/3)^3 - b * c/(6 * a * a) + d/a/2
# v might be very small, resulting in a value v/u^3 > |1|
# causing a numeric error for acos(v/u^3)
# see: x1=10, n1=10, x2=0, n1=10
if(abs(v) < .Machine$double.eps) v <- 0
s <- sqrt((b/a/3)^2 - c/a/3)
u <- ifelse(v>0, 1,-1) * s
w <- (3.141592654 + acos(v/u^3))/3
p1d <- 2 * u * cos(w) - b/a/3
p2d <- p1d - dif
n <- n1 + n2
res <- (p1d * (1 - p1d)/n1 + p2d * (1 - p2d)/n2)
# res <- max(0, res) # might result in a value slightly negative
}
return(sqrt(res))
}
pval <- function(delta){
z <- (est - delta)/.score(p1.hat, n1, p2.hat, n2, delta)
2 * min(pnorm(z), 1-pnorm(z))
}
CI.lower <- max(-1, uniroot(
function(delta) pval(delta) - alpha,
interval = c(-1+1e-6, est-1e-6)
)$root)
CI.upper <- min(1, uniroot(
function(delta) pval(delta) - alpha,
interval = c(est + 1e-6, 1-1e-6)
)$root)
},
"blj" = { # brown-li-jeffrey
p1.dash <- (x1 + 0.5) / (n1+1)
p2.dash <- (x2 + 0.5) / (n2+1)
vd <- p1.dash*(1-p1.dash)/n1 + p2.dash*(1-p2.dash)/n2
term2 <- kappa * sqrt(vd)
est.dash <- p1.dash - p2.dash
CI.lower <- max(-1, est.dash - term2)
CI.upper <- min(1, est.dash + term2)
},
"ha" = { # Hauck-Anderson
term2 <- 1/(2*min(n1,n2)) + kappa * sqrt(p1.hat*(1-p1.hat)/(n1-1) +
p2.hat*(1-p2.hat)/(n2-1))
CI.lower <- max(-1, est - term2)
CI.upper <- min( 1, est + term2 )
},
"mn" = { # Miettinen-Nurminen
.conf <- function(x1, n1, x2, n2, z, lower=FALSE){
p1 <- x1/n1
p2 <- x2/n2
p.hat <- p1 - p2
dp <- 1 + ifelse(lower, 1, -1) * p.hat
i <- 1
while (i <= 50) {
dp <- 0.5 * dp
y <- p.hat + ifelse(lower, -1, 1) * dp
score <- .score(p1, n1, p2, n2, y)
if (score < z) {
p.hat <- y
}
if ((dp < 0.0000001) || (abs(z - score) < 0.000001))
break()
else
i <- i + 1
}
return(y)
}
.score <- function (p1, n1, p2, n2, dif) {
diff <- p1 - p2 - dif
if (abs(diff) == 0) {
res <- 0
}
else {
t <- n2/n1
a <- 1 + t
b <- -(1 + t + p1 + t * p2 + dif * (t + 2))
c <- dif * dif + dif * (2 * p1 + t + 1) + p1 + t * p2
d <- -p1 * dif * (1 + dif)
v <- (b/a/3)^3 - b * c/(6 * a * a) + d/a/2
s <- sqrt((b/a/3)^2 - c/a/3)
u <- ifelse(v>0, 1,-1) * s
w <- (3.141592654 + acos(v/u^3))/3
p1d <- 2 * u * cos(w) - b/a/3
p2d <- p1d - dif
n <- n1 + n2
var <- (p1d * (1 - p1d)/n1 + p2d * (1 - p2d)/n2) * n/(n - 1)
res <- diff^2/var
}
return(res)
}
z = qchisq(conf.level, 1)
CI.lower <- max(-1, .conf(x1, n1, x2, n2, z, TRUE))
CI.upper <- min(1, .conf(x1, n1, x2, n2, z, FALSE))
},
"beal" = {
# experimental code only...
# http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.633.9380&rep=rep1&type=pdf
a <- p1.hat + p2.hat
b <- p1.hat - p2.hat
u <- ((1/n1) + (1/n2)) / 4
v <- ((1/n1) - (1/n2)) / 4
V <- u*((2-a)*a - b^2) + 2*v*(1-a)*b
z <- qchisq(p=1-alpha/2, df = 1)
A <- sqrt(z*(V + z*u^2*(2-a)*a + z*v^2*(1-a)^2))
B <- (b + z*v*(1-a)) / (1+z*u)
CI.lower <- max(-1, B - A / (1 + z*u))
CI.upper <- min(1, B + A / (1 + z*u))
},
"hal" = { # haldane
psi <- (p1.hat + p2.hat) / 2
u <- (1/n1 + 1/n2) / 4
v <- (1/n1 - 1/n2) / 4
z <- kappa
theta <- ((p1.hat - p2.hat) + z^2 * v* (1-2*psi)) / (1+z^2*u)
w <- z / (1+z^2*u) * sqrt(u * (4*psi*(1-psi) - (p1.hat - p2.hat)^2) +
2*v*(1-2*psi) *(p1.hat - p2.hat) +
4*z^2*u^2*(1-psi)*psi + z^2*v^2*(1-2*psi)^2)
c(theta + w, theta - w)
CI.lower <- max(-1, theta - w)
CI.upper <- min(1, theta + w)
},
"jp" = { # jeffery perks
# same as haldane but with other psi
psi <- 0.5 * ((x1 + 0.5) / (n1 + 1) + (x2 + 0.5) / (n2 + 1) )
u <- (1/n1 + 1/n2) / 4
v <- (1/n1 - 1/n2) / 4
z <- kappa
theta <- ((p1.hat - p2.hat) + z^2 * v* (1-2*psi)) / (1+z^2*u)
w <- z / (1+z^2*u) * sqrt(u * (4*psi*(1-psi) - (p1.hat - p2.hat)^2) +
2*v*(1-2*psi) *(p1.hat - p2.hat) +
4*z^2*u^2*(1-psi)*psi + z^2*v^2*(1-2*psi)^2)
c(theta + w, theta - w)
CI.lower <- max(-1, theta - w)
CI.upper <- min(1, theta + w)
},
)
ci <- c(est = est, lwr.ci = min(CI.lower, CI.upper), upr.ci = max(CI.lower, CI.upper))
if(sides=="left")
ci[3] <- 1
else if(sides=="right")
ci[2] <- -1
return(ci)
}
method <- match.arg(arg=method, several.ok = TRUE)
sides <- match.arg(arg=sides, several.ok = TRUE)
# Recycle arguments
lst <- Recycle(x1=x1, n1=n1, x2=x2, n2=n2, conf.level=conf.level, sides=sides, method=method)
res <- t(sapply(1:attr(lst, "maxdim"),
function(i) iBinomDiffCI(x1=lst$x1[i], n1=lst$n1[i], x2=lst$x2[i], n2=lst$n2[i],
conf.level=lst$conf.level[i],
sides=lst$sides[i],
method=lst$method[i])))
# get rownames
lgn <- Recycle(x1=if(is.null(names(x1))) paste("x1", seq_along(x1), sep=".") else names(x1),
n1=if(is.null(names(n1))) paste("n1", seq_along(n1), sep=".") else names(n1),
x2=if(is.null(names(x2))) paste("x2", seq_along(x2), sep=".") else names(x2),
n2=if(is.null(names(n2))) paste("n2", seq_along(n2), sep=".") else names(n2),
conf.level=conf.level, sides=sides, method=method)
xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) length(unique(x)) != 1)]), 1, paste, collapse=":")
rownames(res) <- xn
return(res)
}
BinomRatioCI_old <- function(x1, n1, x2, n2, conf.level = 0.95, method = "katz.log", bonf = FALSE,
tol = .Machine$double.eps^0.25, R = 1000, r = length(x1)) {
# source: asbio::ci.prat by Ken Aho <kenaho1 at gmail.com>
conf <- conf.level
x <- x1; m <- n1; y <- x2; n <- n2
indices <- c("adj.log","bailey","boot","katz.log","koopman","noether","sinh-1")
method <- match.arg(method, indices)
if(any(c(length(m),length(y),length(n))!= length(x))) stop("x1, n1, x2, and n2 vectors must have equal length")
alpha <- 1 - conf
oconf <- conf
conf <- ifelse(bonf == FALSE, conf, 1 - alpha/r)
z.star <- qnorm(1 - (1 - conf)/2)
x2 <- qchisq(conf, 1)
ci.prat1 <- function(x, m, y, n, conf = 0.95, method = "katz.log", bonf = FALSE){
if((x > m)|(y > n)) stop("Use correct parameterization for x1, x2, n1, and n2")
#-------------------------- Adj-log ------------------------------#
if(method == "adj.log"){
if((x == m & y == n)){
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- ((x+0.5)/(m+0.5))/((y+0.5)/(n+0.5)); varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
CIU <- nrat * exp(z.star * sqrt(varhat))
} else if(x == 0 & y == 0){CIL = 0; CIU = Inf; rat = 0; varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
}else{
rat <- (x/m)/(y/n); nrat <- ((x+0.5)/(m+0.5))/((y+0.5)/(n+0.5)); varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
CIU <- nrat * exp(z.star * sqrt(varhat))}
CI <- c(rat, CIL, CIU)
}
#-------------------------------Bailey-----------------------------#
if(method == "bailey"){
rat <- (x/m)/(y/n)
varhat <- ifelse((x == m) & (y == n),(1/(m-0.5)) - (1/(m)) + (1/(n-0.5)) - (1/(n)),(1/(x)) - (1/(m)) + (1/(y)) - (1/(n)))
p.hat1 <- x/m; p.hat2 <- y/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
if(x == 0 | y == 0){
xn <- ifelse(x == 0, 0.5, x)
yn <- ifelse(y == 0, 0.5, y)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
if(xn == m | yn == n){
xn <- ifelse(xn == m, m - 0.5, xn)
yn <- ifelse(yn == n, n - 0.5, yn)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
}
}
if(x == 0 | y == 0){
if(x == 0 & y == 0){
rat <- Inf
CIL <- 0
CIU <- Inf
}
if(x == 0 & y != 0){
CIL <- 0
CIU <- nrat * ((1+ z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}
if(y == 0 & x != 0){
CIU = Inf
CIL <- nrat * ((1- z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}
}else if(x == m | y == n){
xn <- ifelse(x == m, m - 0.5, x)
yn <- ifelse(y == n, n - 0.5, y)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
CIL <- nrat * ((1- z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
CIU <- nrat * ((1+ z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}else{
CIL <- rat * ((1- z.star * sqrt((q.hat1/x) + (q.hat2/y) - (z.star^2 * q.hat1 * q.hat2)/(9 * x * y))/3)/((1 - (z.star^2 * q.hat2)/(9 * y))))^3
CIU <- rat * ((1+ z.star * sqrt((q.hat1/x) + (q.hat2/y) - (z.star^2 * q.hat1 * q.hat2)/(9 * x * y))/3)/((1 - (z.star^2 * q.hat2)/(9 * y))))^3
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Boot ------------------------------#
if(method == "boot"){
rat <- (x/m)/(y/n)
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {CIL <- 0; rat <- (x/m)/(y/n); x <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIU <- nrat * exp(z.star * sqrt(varhat))}
if(x != 0 & y == 0) {CIU <- Inf; rat <- (x/m)/(y/n); y <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))}
} else{
num.data <- c(rep(1, x), rep(0, m - x))
den.data <- c(rep(1, y), rep(0, n - y))
nd <- matrix(ncol = R, nrow = m)
dd <- matrix(ncol = R, nrow = n)
brat <- 1:R
for(i in 1L:R){
nd[,i] <- sample(num.data, m, replace = TRUE)
dd[,i] <- sample(den.data, n, replace = TRUE)
brat[i] <- (sum(nd[,i])/m)/(sum(dd[,i])/n)
}
alpha <- 1 - conf
CIU <- quantile(brat, 1 - alpha/2, na.rm = TRUE)
CIL <- quantile(brat, alpha/2, na.rm = TRUE)
varhat <- var(brat)
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Katz-log ------------------------------#
if(method == "katz.log"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {CIL <- 0; rat <- (x/m)/(y/n); x <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIU <- nrat * exp(z.star * sqrt(varhat))}
if(x != 0 & y == 0) {CIU <- Inf; rat <- (x/m)/(y/n); y <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n); CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n); CIU <- nrat * exp(z.star * sqrt(varhat))
}
} else
{rat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- rat * exp(-1 * z.star * sqrt(varhat))
CIU <- rat * exp(z.star * sqrt(varhat))}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Koopman ------------------------------#
if(method == "koopman"){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA
} else {
a1 = n * (n * (n + m) * x + m * (n + x) * (z.star^2))
a2 = -n * (n * m * (y + x) + 2 * (n + m) * y *
x + m * (n + y + 2 * x) * (z.star^2))
a3 = 2 * n * m * y * (y + x) + (n + m) * (y^2) *
x + n * m * (y + x) * (z.star^2)
a4 = -m * (y^2) * (y + x)
b1 = a2/a1; b2 = a3/a1; b3 = a4/a1
c1 = b2 - (b1^2)/3; c2 = b3 - b1 * b2/3 + 2 * (b1^3)/27
ceta = suppressWarnings(acos(sqrt(27) * c2/(2 * c1 * sqrt(-c1))))
t1 <- suppressWarnings(-2 * sqrt(-c1/3) * cos(pi/3 - ceta/3))
t2 <- suppressWarnings(-2 * sqrt(-c1/3) * cos(pi/3 + ceta/3))
t3 <- suppressWarnings(2 * sqrt(-c1/3) * cos(ceta/3))
p01 = t1 - b1/3; p02 = t2 - b1/3; p03 = t3 - b1/3
p0sum = p01 + p02 + p03; p0up = min(p01, p02, p03); p0low = p0sum - p0up - max(p01, p02, p03)
U <- function(a){
p.hatf <- function(a){
(a * (m + y) + x + n - ((a * (m + y) + x + n)^2 - 4 * a * (m + n) * (x + y))^0.5)/(2 * (m + n))
}
p.hat <- p.hatf(a)
(((x - m * p.hat)^2)/(m * p.hat * (1 - p.hat)))*(1 + (m * (a - p.hat))/(n * (1 - p.hat))) - x2
}
rat <- (x/m)/(y/n); nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n)
if((x == 0) & (y != 0)) {nrat <- ((x + 0.5)/m)/(y/n); varhat <- (1/(x + 0.5)) - (1/m) + (1/y) - (1/n)}
if((y == 0) & (x != 0)) {nrat <- (x/m)/((y + 0.5)/n); varhat <- (1/x) - (1/m) + (1/(y + 0.5)) - (1/n)}
if((y == n) & (x == m)) {nrat <- 1; varhat <- (1/(m - 0.5)) - (1/m) + 1/(n - 0.5) - (1/n)}
La <- nrat * exp(-1 * z.star * sqrt(varhat)) * 1/4
Lb <- nrat
Ha <- nrat
Hb <- nrat * exp(z.star * sqrt(varhat)) * 4
#----------------------------------------------------------------------------#
if((x != 0) & (y == 0)) {
if(x == m){
CIL = (1 - (m - x) * (1 - p0low)/(y + m - (n + m) * p0low))/p0low
CIU <- Inf
}
else{
CIL <- uniroot(U, c(La, Lb), tol=tol)$root
CIU <- Inf
}
}
#------------------------------------------------------------#
if((x == 0) & (y != n)) {
CIU <- uniroot(U, c(Ha, Hb), tol=tol)$root
CIL <- 0
}
#------------------------------------------------------------#
if(((x == m)|(y == n)) & (y != 0)){
if((x == m)&(y == n)){
U.0 <- function(a){if(a <= 1) {m * (1 - a)/a - x2}
else{(n * (a - 1)) - x2}
}
CIL <- uniroot(U.0, c(La, rat), tol = tol)$root
CIU <- uniroot(U.0, c(rat, Hb), tol = tol)$root
}
#------------------------------------------------------------#
if((x == m) & (y != n)){
phat1 = x/m; phat2 = y/n
phihat = phat2/phat1
phiu = 1.1 * phihat
r = 0
while (r >= -z.star) {
a = (m + n) * phiu
b = -((x + n) * phiu + y + m)
c = x + y
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phiu
q2hat = 1 - p2hat
var = (m * n * p2hat)/(n * (phiu - p2hat) +
m * q2hat)
r = ((y - n * p2hat)/q2hat)/sqrt(var)
phiu1 = phiu
phiu = 1.0001 * phiu1
}
CIU = (1 - (m - x) * (1 - p0up)/(y + m - (n + m) * p0up))/p0up
CIL = 1/phiu1
}
#------------------------------------------------------------#
if((y == n) & (x != m)){
p.hat2 = y/n; p.hat1 = x/m; phihat = p.hat1/p.hat2
phil = 0.95 * phihat; r = 0
if(x != 0){
while(r <= z.star) {
a = (n + m) * phil
b = -((y + m) * phil + x + n)
c = y + x
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phil
q2hat = 1 - p2hat
var = (n * m * p2hat)/(m * (phil - p2hat) +
n * q2hat)
r = ((x - m * p2hat)/q2hat)/sqrt(var)
CIL = phil
phil = CIL/1.0001
}
}
phiu = 1.1 * phihat
if(x == 0){CIL = 0; phiu <- ifelse(n < 100, 0.01, 0.001)}
r = 0
while(r >= -z.star) {
a = (n + m) * phiu
b = -((y + m) * phiu + x + n)
c = y + x
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phiu
q2hat = 1 - p2hat
var = (n * m * p2hat)/(m * (phiu - p2hat) +
n * q2hat)
r = ((x - m * p2hat)/q2hat)/sqrt(var)
phiu1 = phiu
phiu = 1.0001 * phiu1
}
CIU <- phiu1
}
} else if((y != n) & (x != m) & (x != 0) & (y != 0)){
CIL <- uniroot(U, c(La, Lb), tol=tol)$root
CIU <- uniroot(U, c(Ha, Hb), tol=tol)$root
}
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Noether ------------------------------#
if(method == "noether"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; se.hat <- NA; varhat = NA}
if(x == 0 & y != 0) {rat <- (x/m)/(y/n); CIL <- 0; x <- 0.5
nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIU <- nrat + z.star * se.hat}
if(x != 0 & y == 0) {rat <- Inf; CIU <- Inf; y <- 0.5
nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIL <- nrat - z.star * se.hat}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIU <- nrat + z.star * se.hat
CIL <- nrat - z.star * se.hat
}
} else
{
rat <- (x/m)/(y/n)
se.hat <- rat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIL <- rat - z.star * se.hat
CIU <- rat + z.star * se.hat
}
varhat <- ifelse(is.na(se.hat), NA, se.hat^2)
CI <- c(rat, max(0,CIL), CIU)
}
#------------------------- sinh-1 -----------------------------#
if(method == "sinh-1"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {rat <- (x/m)/(y/n); CIL <- 0; x <- z.star
nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIU <- exp(log(nrat) + varhat)}
if(x != 0 & y == 0) {rat = Inf; CIU <- Inf; y <- z.star
nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(nrat) - varhat)}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(nrat) - varhat)
CIU <- exp(log(nrat) + varhat)
}
} else
{rat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(rat) - varhat)
CIU <- exp(log(rat) + varhat)
}
CI <- c(rat, CIL, CIU)
}
#------------------------Results ------------------------------#
res <- list(CI = CI, varhat = varhat)
res
}
CI <- matrix(ncol = 3, nrow = length(x1))
vh <- rep(NA, length(x1))
for(i in 1L : length(x1)){
temp <- ci.prat1(x = x[i], m = m[i], y = y[i], n = n[i], conf = conf, method = method, bonf = bonf)
CI[i,] <- temp$CI
vh[i] <- temp$varhat
}
CI <- data.frame(CI)
if(length(x1) == 1) row.names(CI) <- ""
head <- paste(paste(as.character(oconf * 100),"%",sep=""), c("Confidence interval for ratio of binomial proportions"))
if(method == "adj.log")head <- paste(head,"(method=adj-log)")
if(method == "bailey")head <- paste(head,"(method=Bailey)")
if(method == "boot")head <- paste(head,"(method=percentile bootstrap)")
if(method == "katz.log")head <- paste(head,"(method=Katz-log)")
if(method == "koopman")head <- paste(head,"(method=Koopman)")
if(method == "noether")head <- paste(head,"(method=Noether)")
if(method == "sinh")head <- paste(head,"(method=sinh^-1)")
if(bonf == TRUE)head <- paste(head, "\n Bonferroni simultaneous intervals, r = ", bquote(.(r)),
"\n Marginal confidence = ", bquote(.(conf)), "\n", sep = "")
ends <- c("Estimate", paste(as.character(c((1-oconf)/2, 1-((1-oconf)/2))*100), "%", sep=""))
# res <- list(varhat = vh, ci = CI, ends = ends, head = head)
# class(res) <- "ci"
res <- data.matrix(CI)
dimnames(res) <- list(NULL, c("est","lwr.ci","upr.ci"))
res
}
BinomRatioCI <- function(x1, n1, x2, n2, conf.level = 0.95, sides = c("two.sided","left","right"),
method =c("katz.log","adj.log","bailey","koopman","noether","sinh-1","boot"),
tol = .Machine$double.eps^0.25, R = 1000) {
# source: asbio::ci.prat by Ken Aho <kenaho1 at gmail.com>
iBinomRatioCI <- function(x, m, y, n, conf, sides, method) {
if((x > m)|(y > n)) stop("Use correct parameterization for x1, x2, n1, and n2")
method <- match.arg(method, c("katz.log","adj.log","bailey","koopman","noether","sinh-1","boot"))
if(sides!="two.sided")
conf <- 1 - 2*(1-conf)
alpha <- 1 - conf
z.star <- qnorm(1 - (1 - conf)/2)
x2 <- qchisq(conf, 1)
#-------------------------- Adj-log ------------------------------#
if(method == "adj.log"){
if((x == m & y == n)){
rat <- (x/m)/(y/n)
x <- m - 0.5
y <- n - 0.5
nrat <- ((x+0.5)/(m+0.5))/((y+0.5)/(n+0.5))
varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
CIU <- nrat * exp(z.star * sqrt(varhat))
} else if(x == 0 & y == 0){
CIL = 0
CIU = Inf
rat = 0
varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
} else {
rat <- (x/m)/(y/n)
nrat <- ((x+0.5)/(m+0.5))/((y+0.5)/(n+0.5))
varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
CIU <- nrat * exp(z.star * sqrt(varhat))
}
CI <- c(rat, CIL, CIU)
}
#-------------------------------Bailey-----------------------------#
if(method == "bailey"){
rat <- (x/m)/(y/n)
varhat <- ifelse((x == m) & (y == n),(1/(m-0.5)) - (1/(m)) + (1/(n-0.5)) - (1/(n)),(1/(x)) - (1/(m)) + (1/(y)) - (1/(n)))
p.hat1 <- x/m; p.hat2 <- y/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
if(x == 0 | y == 0){
xn <- ifelse(x == 0, 0.5, x)
yn <- ifelse(y == 0, 0.5, y)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
if(xn == m | yn == n){
xn <- ifelse(xn == m, m - 0.5, xn)
yn <- ifelse(yn == n, n - 0.5, yn)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
}
}
if(x == 0 | y == 0){
if(x == 0 & y == 0){
rat <- Inf
CIL <- 0
CIU <- Inf
}
if(x == 0 & y != 0){
CIL <- 0
CIU <- nrat * ((1+ z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}
if(y == 0 & x != 0){
CIU = Inf
CIL <- nrat * ((1- z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}
}else if(x == m | y == n){
xn <- ifelse(x == m, m - 0.5, x)
yn <- ifelse(y == n, n - 0.5, y)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
CIL <- nrat * ((1- z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
CIU <- nrat * ((1+ z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}else{
CIL <- rat * ((1- z.star * sqrt((q.hat1/x) + (q.hat2/y) - (z.star^2 * q.hat1 * q.hat2)/(9 * x * y))/3)/((1 - (z.star^2 * q.hat2)/(9 * y))))^3
CIU <- rat * ((1+ z.star * sqrt((q.hat1/x) + (q.hat2/y) - (z.star^2 * q.hat1 * q.hat2)/(9 * x * y))/3)/((1 - (z.star^2 * q.hat2)/(9 * y))))^3
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Boot ------------------------------#
if(method == "boot"){
rat <- (x/m)/(y/n)
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {CIL <- 0; rat <- (x/m)/(y/n); x <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIU <- nrat * exp(z.star * sqrt(varhat))}
if(x != 0 & y == 0) {CIU <- Inf; rat <- (x/m)/(y/n); y <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))}
} else{
num.data <- c(rep(1, x), rep(0, m - x))
den.data <- c(rep(1, y), rep(0, n - y))
nd <- matrix(ncol = R, nrow = m)
dd <- matrix(ncol = R, nrow = n)
brat <- 1:R
for(i in 1L:R){
nd[,i] <- sample(num.data, m, replace = TRUE)
dd[,i] <- sample(den.data, n, replace = TRUE)
brat[i] <- (sum(nd[,i])/m)/(sum(dd[,i])/n)
}
alpha <- 1 - conf
CIU <- quantile(brat, 1 - alpha/2, na.rm = TRUE)
CIL <- quantile(brat, alpha/2, na.rm = TRUE)
varhat <- var(brat)
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Katz-log ------------------------------#
if(method == "katz.log"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {CIL <- 0; rat <- (x/m)/(y/n); x <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIU <- nrat * exp(z.star * sqrt(varhat))}
if(x != 0 & y == 0) {CIU <- Inf; rat <- (x/m)/(y/n); y <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n); CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n); CIU <- nrat * exp(z.star * sqrt(varhat))
}
} else
{rat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- rat * exp(-1 * z.star * sqrt(varhat))
CIU <- rat * exp(z.star * sqrt(varhat))}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Koopman ------------------------------#
if(method == "koopman"){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA
} else {
a1 = n * (n * (n + m) * x + m * (n + x) * (z.star^2))
a2 = -n * (n * m * (y + x) + 2 * (n + m) * y *
x + m * (n + y + 2 * x) * (z.star^2))
a3 = 2 * n * m * y * (y + x) + (n + m) * (y^2) *
x + n * m * (y + x) * (z.star^2)
a4 = -m * (y^2) * (y + x)
b1 = a2/a1; b2 = a3/a1; b3 = a4/a1
c1 = b2 - (b1^2)/3; c2 = b3 - b1 * b2/3 + 2 * (b1^3)/27
ceta = suppressWarnings(acos(sqrt(27) * c2/(2 * c1 * sqrt(-c1))))
t1 <- suppressWarnings(-2 * sqrt(-c1/3) * cos(pi/3 - ceta/3))
t2 <- suppressWarnings(-2 * sqrt(-c1/3) * cos(pi/3 + ceta/3))
t3 <- suppressWarnings(2 * sqrt(-c1/3) * cos(ceta/3))
p01 = t1 - b1/3; p02 = t2 - b1/3; p03 = t3 - b1/3
p0sum = p01 + p02 + p03; p0up = min(p01, p02, p03); p0low = p0sum - p0up - max(p01, p02, p03)
U <- function(a){
p.hatf <- function(a){
(a * (m + y) + x + n - ((a * (m + y) + x + n)^2 - 4 * a * (m + n) * (x + y))^0.5)/(2 * (m + n))
}
p.hat <- p.hatf(a)
(((x - m * p.hat)^2)/(m * p.hat * (1 - p.hat)))*(1 + (m * (a - p.hat))/(n * (1 - p.hat))) - x2
}
rat <- (x/m)/(y/n); nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n)
if((x == 0) & (y != 0)) {nrat <- ((x + 0.5)/m)/(y/n); varhat <- (1/(x + 0.5)) - (1/m) + (1/y) - (1/n)}
if((y == 0) & (x != 0)) {nrat <- (x/m)/((y + 0.5)/n); varhat <- (1/x) - (1/m) + (1/(y + 0.5)) - (1/n)}
if((y == n) & (x == m)) {nrat <- 1; varhat <- (1/(m - 0.5)) - (1/m) + 1/(n - 0.5) - (1/n)}
La <- nrat * exp(-1 * z.star * sqrt(varhat)) * 1/4
Lb <- nrat
Ha <- nrat
Hb <- nrat * exp(z.star * sqrt(varhat)) * 4
#----------------------------------------------------------------------------#
if((x != 0) & (y == 0)) {
if(x == m){
CIL = (1 - (m - x) * (1 - p0low)/(y + m - (n + m) * p0low))/p0low
CIU <- Inf
}
else{
CIL <- uniroot(U, c(La, Lb), tol=tol)$root
CIU <- Inf
}
}
#------------------------------------------------------------#
if((x == 0) & (y != n)) {
CIU <- uniroot(U, c(Ha, Hb), tol=tol)$root
CIL <- 0
}
#------------------------------------------------------------#
if(((x == m)|(y == n)) & (y != 0)){
if((x == m)&(y == n)){
U.0 <- function(a){if(a <= 1) {m * (1 - a)/a - x2}
else{(n * (a - 1)) - x2}
}
CIL <- uniroot(U.0, c(La, rat), tol = tol)$root
CIU <- uniroot(U.0, c(rat, Hb), tol = tol)$root
}
#------------------------------------------------------------#
if((x == m) & (y != n)){
phat1 = x/m; phat2 = y/n
phihat = phat2/phat1
phiu = 1.1 * phihat
r = 0
while (r >= -z.star) {
a = (m + n) * phiu
b = -((x + n) * phiu + y + m)
c = x + y
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phiu
q2hat = 1 - p2hat
var = (m * n * p2hat)/(n * (phiu - p2hat) +
m * q2hat)
r = ((y - n * p2hat)/q2hat)/sqrt(var)
phiu1 = phiu
phiu = 1.0001 * phiu1
}
CIU = (1 - (m - x) * (1 - p0up)/(y + m - (n + m) * p0up))/p0up
CIL = 1/phiu1
}
#------------------------------------------------------------#
if((y == n) & (x != m)){
p.hat2 = y/n; p.hat1 = x/m; phihat = p.hat1/p.hat2
phil = 0.95 * phihat; r = 0
if(x != 0){
while(r <= z.star) {
a = (n + m) * phil
b = -((y + m) * phil + x + n)
c = y + x
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phil
q2hat = 1 - p2hat
var = (n * m * p2hat)/(m * (phil - p2hat) +
n * q2hat)
r = ((x - m * p2hat)/q2hat)/sqrt(var)
CIL = phil
phil = CIL/1.0001
}
}
phiu = 1.1 * phihat
if(x == 0){CIL = 0; phiu <- ifelse(n < 100, 0.01, 0.001)}
r = 0
while(r >= -z.star) {
a = (n + m) * phiu
b = -((y + m) * phiu + x + n)
c = y + x
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phiu
q2hat = 1 - p2hat
var = (n * m * p2hat)/(m * (phiu - p2hat) +
n * q2hat)
r = ((x - m * p2hat)/q2hat)/sqrt(var)
phiu1 = phiu
phiu = 1.0001 * phiu1
}
CIU <- phiu1
}
} else if((y != n) & (x != m) & (x != 0) & (y != 0)){
CIL <- uniroot(U, c(La, Lb), tol=tol)$root
CIU <- uniroot(U, c(Ha, Hb), tol=tol)$root
}
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Noether ------------------------------#
if(method == "noether"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; se.hat <- NA; varhat = NA}
if(x == 0 & y != 0) {rat <- (x/m)/(y/n); CIL <- 0; x <- 0.5
nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIU <- nrat + z.star * se.hat}
if(x != 0 & y == 0) {rat <- Inf; CIU <- Inf; y <- 0.5
nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIL <- nrat - z.star * se.hat}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIU <- nrat + z.star * se.hat
CIL <- nrat - z.star * se.hat
}
} else
{
rat <- (x/m)/(y/n)
se.hat <- rat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIL <- rat - z.star * se.hat
CIU <- rat + z.star * se.hat
}
varhat <- ifelse(is.na(se.hat), NA, se.hat^2)
CI <- c(rat, max(0,CIL), CIU)
}
#------------------------- sinh-1 -----------------------------#
if(method == "sinh-1"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {rat <- (x/m)/(y/n); CIL <- 0; x <- z.star
nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIU <- exp(log(nrat) + varhat)}
if(x != 0 & y == 0) {rat = Inf; CIU <- Inf; y <- z.star
nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(nrat) - varhat)}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(nrat) - varhat)
CIU <- exp(log(nrat) + varhat)
}
} else
{rat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(rat) - varhat)
CIU <- exp(log(rat) + varhat)
}
CI <- c(rat, CIL, CIU)
}
#------------------------Results ------------------------------#
# res <- list(CI = CI, varhat = varhat)
CI
}
if(missing(sides)) sides <- match.arg(sides)
if(missing(method)) method <- match.arg(method)
# Recycle arguments
lst <- Recycle(x1=x1, n1=n1, x2=x2, n2=n2, conf.level=conf.level, sides=sides, method=method)
# iBinomRatioCI <- function(x, m, y, n, conf, method){
res <- t(sapply(1:attr(lst, "maxdim"),
function(i) iBinomRatioCI(x=lst$x1[i], m=lst$n1[i], y=lst$x2[i], n=lst$n2[i],
conf=lst$conf.level[i],
sides=lst$sides[i],
method=lst$method[i])))
# get rownames
lgn <- Recycle(x1=if(is.null(names(x1))) paste("x1", seq_along(x1), sep=".") else names(x1),
n1=if(is.null(names(n1))) paste("n1", seq_along(n1), sep=".") else names(n1),
x2=if(is.null(names(x2))) paste("x2", seq_along(x2), sep=".") else names(x2),
n2=if(is.null(names(n2))) paste("n2", seq_along(n2), sep=".") else names(n2),
conf.level=conf.level, sides=sides, method=method)
xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) length(unique(x)) != 1)]), 1, paste, collapse=":")
return(SetNames(res,
rownames=xn,
colnames=c("est", "lwr.ci", "upr.ci")))
}
MultinomCI <- function(x, conf.level = 0.95, sides = c("two.sided","left","right"),
method = c("sisonglaz", "cplus1", "goodman", "wald", "waldcc", "wilson", "qh", "fs")) {
# Code mainly by:
# Pablo J. Villacorta Iglesias <pjvi@decsai.ugr.es>\n
# Department of Computer Science and Artificial Intelligence, University of Granada (Spain)
.moments <- function(c, lambda){
a <- lambda + c
b <- lambda - c
if(b < 0) b <- 0
if(b > 0) den <- ppois(a, lambda) - ppois(b-1, lambda)
if(b == 0) den <- ppois(a,lambda)
mu <- mat.or.vec(4,1)
mom <- mat.or.vec(5,1)
for(r in 1:4){
poisA <- 0
poisB <- 0
if((a-r) >=0){ poisA <- ppois(a,lambda)-ppois(a-r,lambda) }
if((a-r) < 0){ poisA <- ppois(a,lambda) }
if((b-r-1) >=0){ poisB <- ppois(b-1,lambda)-ppois(b-r-1,lambda) }
if((b-r-1) < 0 && (b-1)>=0){ poisB <- ppois(b-1,lambda) }
if((b-r-1) < 0 && (b-1) < 0){ poisB <- 0 }
mu[r] <- (lambda^r)*(1-(poisA-poisB)/den)
}
mom[1] <- mu[1]
mom[2] <- mu[2] + mu[1] - mu[1]^2
mom[3] <- mu[3] + mu[2]*(3-3*mu[1]) + (mu[1]-3*mu[1]^2+2*mu[1]^3)
mom[4] <- mu[4] + mu[3]*(6-4*mu[1]) + mu[2]*(7-12*mu[1]+6*mu[1]^2)+mu[1]-4*mu[1]^2+6*mu[1]^3-3*mu[1]^4
mom[5] <- den
return(mom)
}
.truncpoi <- function(c, x, n, k){
m <- matrix(0, k, 5)
for(i in 1L:k){
lambda <- x[i]
mom <- .moments(c, lambda)
for(j in 1L:5L){ m[i,j] <- mom[j] }
}
for(i in 1L:k){ m[i, 4] <- m[i, 4] - 3 * m[i, 2]^2 }
s <- colSums(m)
s1 <- s[1]
s2 <- s[2]
s3 <- s[3]
s4 <- s[4]
probn <- 1/(ppois(n,n)-ppois(n-1,n))
z <- (n-s1)/sqrt(s2)
g1 <- s3/(s2^(3/2))
g2 <- s4/(s2^2)
poly <- 1 + g1*(z^3-3*z)/6 + g2*(z^4-6*z^2+3)/24
+ g1^2*(z^6-15*z^4 + 45*z^2-15)/72
f <- poly*exp(-z^2/2)/(sqrt(2)*gamma(0.5))
probx <- 1
for(i in 1L:k){ probx <- probx * m[i,5] }
return(probn * probx * f / sqrt(s2))
}
n <- sum(x, na.rm=TRUE)
k <- length(x)
p <- x/n
if (missing(method)) method <- "sisonglaz"
if(missing(sides)) sides <- "two.sided"
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
method <- match.arg(arg = method, choices = c("sisonglaz", "cplus1", "goodman", "wald", "waldcc", "wilson", "qh", "fs"))
if(method == "goodman") {
# erroneous: q.chi <- qchisq(conf.level, k - 1)
# corrected on
q.chi <- qchisq(1 - (1-conf.level)/k, df = 1)
lci <- (q.chi + 2*x - sqrt(q.chi*(q.chi + 4*x*(n-x)/n))) / (2*(n+q.chi))
uci <- (q.chi + 2*x + sqrt(q.chi*(q.chi + 4*x*(n-x)/n))) / (2*(n+q.chi))
res <- cbind(est=p, lwr.ci=pmax(0, lci), upr.ci=pmin(1, uci))
} else if(method == "wald") {
q.chi <- qchisq(conf.level, 1)
lci <- p - sqrt(q.chi * p * (1 - p)/n)
uci <- p + sqrt(q.chi * p * (1 - p)/n)
res <- cbind(est=p, lwr.ci=pmax(0, lci), upr.ci=pmin(1, uci))
} else if(method == "waldcc") {
q.chi <- qchisq(conf.level, 1)
lci <- p - sqrt(q.chi * p * (1 - p)/n) - 1/(2*n)
uci <- p + sqrt(q.chi * p * (1 - p)/n) + 1/(2*n)
res <- cbind(est=p, lwr.ci=pmax(0, lci), upr.ci=pmin(1, uci))
} else if(method == "wilson") {
q.chi <- qchisq(conf.level, 1)
lci <- (q.chi + 2*x - sqrt(q.chi^2 + 4*x*q.chi * (1 - p))) / (2*(q.chi + n))
uci <- (q.chi + 2*x + sqrt(q.chi^2 + 4*x*q.chi * (1 - p))) / (2*(q.chi + n))
res <- cbind(est=p, lwr.ci=pmax(0, lci), upr.ci=pmin(1, uci))
} else if (method == "fs") {
# references Fitzpatrick, S. and Scott, A. (1987). Quick simultaneous confidence
# interval for multinomial proportions.
# Journal of American Statistical Association 82(399): 875-878.
q.snorm <- qnorm(1-(1 - conf.level)/2)
lci <- p - q.snorm / (2 * sqrt(n))
uci <- p + q.snorm / (2 * sqrt(n))
res <- cbind(est = p, lwr.ci = pmax(0, lci), upr.ci = pmin(1, uci))
} else if (method == "qh") {
# references Quesensberry, C.P. and Hurst, D.C. (1964).
# Large Sample Simultaneous Confidence Intervals for
# Multinational Proportions. Technometrics, 6: 191-195.
q.chi <- qchisq(conf.level, df = k-1)
lci <- (q.chi + 2*x - sqrt(q.chi^2 + 4*x*q.chi*(1 - p)))/(2*(q.chi+n))
uci <- (q.chi + 2*x + sqrt(q.chi^2 + 4*x*q.chi*(1 - p)))/(2*(q.chi+n))
res <- cbind(est = p, lwr.ci = pmax(0, lci), upr.ci = pmin(1, uci))
} else { # sisonglaz, cplus1
const <- 0
pold <- 0
for(cc in 1:n){
poi <- .truncpoi(cc, x, n, k)
if(poi > conf.level && pold < conf.level) {
const <- cc
break
}
pold <- poi
}
delta <- (conf.level - pold)/(poi - pold)
const <- const - 1
if(method == "sisonglaz") {
res <- cbind(est = p, lwr.ci = pmax(0, p - const/n), upr.ci = pmin(1, p + const/n + 2*delta/n))
} else if(method == "cplus1") {
res <- cbind(est = p, lwr.ci = pmax(0, p - const/n - 1/n), upr.ci = pmin(1,p + const/n + 1/n))
}
}
if(sides=="left")
res[3] <- 1
else if(sides=="right")
res[2] <- 0
return(res)
}
# Confidence Intervals for Poisson mean
PoissonCI <- function(x, n = 1, conf.level = 0.95, sides = c("two.sided","left","right"),
method = c("exact","score", "wald","byar")) {
if(missing(method)) method <- "exact"
if(missing(sides)) sides <- "two.sided"
iPoissonCI <- function(x, n = 1, conf.level = 0.95, sides = c("two.sided","left","right"),
method = c("exact","score", "wald","byar")) {
# ref: http://www.ijmo.org/papers/189-S083.pdf but wacklig!!!
# http://www.math.montana.edu/~rjboik/classes/502/ci.pdf
# http://www.ine.pt/revstat/pdf/rs120203.pdf
# http://www.pvamu.edu/include/Math/AAM/AAM%20Vol%206,%20Issue%201%20(June%202011)/06_%20Kibria_AAM_R308_BK_090110_Vol_6_Issue_1.pdf
# see also: pois.conf.int {epitools}
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
if(missing(method)) method <- "score"
if(length(conf.level) != 1) stop("'conf.level' has to be of length 1 (confidence level)")
if(conf.level < 0.5 | conf.level > 1) stop("'conf.level' has to be in [0.5, 1]")
alpha <- 1 - conf.level
z <- qnorm(1-alpha/2)
lambda <- x/n
switch( match.arg(arg=method, choices=c("exact","score", "wald","byar"))
, "exact" = {
ci <- poisson.test(x, n, conf.level = conf.level)$conf.int
lwr.ci <- ci[1]
upr.ci <- ci[2]
}
, "score" = {
term1 <- (x + z^2/2)/n
term2 <- z * n^-0.5 * sqrt(x/n + z^2/(4*n))
lwr.ci <- term1 - term2
upr.ci <- term1 + term2
}
, "wald" = {
term2 <- z*sqrt(lambda/n)
lwr.ci <- lambda - term2
upr.ci <- lambda + term2
}
, "byar" = {
xcc <- x + 0.5
zz <- (z/3) * sqrt(1/xcc)
lwr.ci <- (xcc * (1 - 1/(9 * xcc) - zz)^3)/n
upr.ci <- (xcc * (1 - 1/(9 * xcc) + zz)^3)/n
}
# agresti-coull is the same as score
# , "agresti-coull" = {
# lwr.ci <- lambda + z^2/(2*n) - z*sqrt(lambda/n + z^2/(4*n^2))
# upr.ci <- lambda + z^2/(2*n) + z*sqrt(lambda/n + z^2/(4*n^2))
#
# }
# garwood is the same as exact, check that!!
# , "garwood" = {
# lwr.ci <- qchisq((1 - conf.level)/2, 2*x)/(2*n)
# upr.ci <- qchisq(1 - (1 - conf.level)/2, 2*(x + 1))/(2*n)
# }
)
ci <- c( est=lambda, lwr.ci=lwr.ci, upr.ci=upr.ci )
if(sides=="left")
ci[3] <- Inf
else if(sides=="right")
ci[2] <- -Inf
return(ci)
}
# handle vectors
# which parameter has the highest dimension
lst <- list(x=x, n=n, conf.level=conf.level, sides=sides, method=method)
maxdim <- max(unlist(lapply(lst, length)))
# recycle all params to maxdim
lgp <- lapply( lst, rep, length.out=maxdim )
res <- sapply(1:maxdim, function(i) iPoissonCI(x=lgp$x[i], n=lgp$n[i],
conf.level=lgp$conf.level[i],
sides=lgp$sides[i],
method=lgp$method[i]))
rownames(res)[1] <- c("est")
lgn <- Recycle(x=if(is.null(names(x))) paste("x", seq_along(x), sep=".") else names(x),
n=if(is.null(names(n))) paste("n", seq_along(n), sep=".") else names(n),
conf.level=conf.level, sides=sides, method=method)
xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) length(unique(x)) != 1)]), 1, paste, collapse=":")
colnames(res) <- xn
return(t(res))
}
QuantileCI <- function(x, probs=seq(0, 1, .25), conf.level = 0.95, sides = c("two.sided", "left", "right"),
na.rm = FALSE, method = c("exact", "boot"), R = 999) {
.QuantileCI <- function(x, prob, conf.level = 0.95, sides = c("two.sided", "left", "right")) {
# Near-symmetric distribution-free confidence interval for a quantile `q`.
# https://stats.stackexchange.com/questions/99829/how-to-obtain-a-confidence-interval-for-a-percentile
# Search over a small range of upper and lower order statistics for the
# closest coverage to 1-alpha (but not less than it, if possible).
n <- length(x)
alpha <- 1- conf.level
if(sides == "two.sided"){
u <- qbinom(p = 1-alpha/2, size = n, prob = prob) + (-2:2) + 1
l <- qbinom(p = alpha/2, size = n, prob = prob) + (-2:2)
u[u > n] <- Inf
l[l < 0] <- -Inf
coverage <- outer(l, u, function(a,b) pbinom(b-1, n, prob = prob) - pbinom(a-1, n, prob=prob))
if (max(coverage) < 1-alpha) i <- which(coverage==max(coverage)) else
i <- which(coverage == min(coverage[coverage >= 1-alpha]))
# minimal difference
i <- i[1]
# order statistics and the actual coverage.
u <- rep(u, each=5)[i]
l <- rep(l, 5)[i]
coverage <- coverage[i]
} else if(sides == "left"){
l <- qbinom(p = alpha, size = n, prob = prob)
u <- Inf
coverage <- 1 - pbinom(q = l-1, size = n, prob = prob)
} else if(sides == "right"){
l <- -Inf
u <- qbinom(p = 1-alpha, size = n, prob = prob)
coverage <- pbinom(q = u, size = n, prob = prob)
}
# get the values
if(prob %nin% c(0,1))
s <- sort(x, partial= c(u, l)[is.finite(c(u, l))])
else
s <- sort(x)
res <- c(lwr.ci=s[l], upr.ci=s[u])
attr(res, "conf.level") <- coverage
if(sides=="left")
res[2] <- Inf
else if(sides=="right")
res[1] <- -Inf
return(res)
}
if (na.rm) x <- na.omit(x)
if(anyNA(x))
stop("missing values and NaN's not allowed if 'na.rm' is FALSE")
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
method <- match.arg(arg=method, choices=c("exact","boot"))
switch( method
, "exact" = { # this is the SAS-way to do it
r <- lapply(probs, function(p) .QuantileCI(x, prob=p, conf.level = conf.level, sides=sides))
coverage <- sapply(r, function(z) attr(z, "conf.level"))
r <- do.call(rbind, r)
attr(r, "conf.level") <- coverage
}
, "boot" = {
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
r <- t(sapply(probs,
function(p) {
boot.med <- boot(x, function(x, d) quantile(x[d], probs=p, na.rm=na.rm), R=R)
boot.ci(boot.med, conf=conf.level, type="basic")[[4]][4:5]
}))
} )
qq <- quantile(x, probs=probs, na.rm=na.rm)
if(length(probs)==1){
res <- c(qq, r)
names(res) <- c("est","lwr.ci","upr.ci")
# report the conf.level which can deviate from the required one
if(method=="exact") attr(res, "conf.level") <- attr(r, "conf.level")
} else {
res <- cbind(qq, r)
colnames(res) <- c("est","lwr.ci","upr.ci")
# report the conf.level which can deviate from the required one
if(method=="exact")
# report coverages for all probs
attr(res, "conf.level") <- attr(r, "conf.level")
}
return( res )
}
# standard error of mean
MeanSE <- function(x, sd = NULL, na.rm = FALSE) {
if(na.rm) x <- na.omit(x)
if(is.null(sd)) s <- sd(x)
s/sqrt(length(x))
}
MeanCIn <- function(ci, sd, interval=c(2, 1e5), conf.level=0.95, norm=FALSE,
tol = .Machine$double.eps^0.5) {
width <- diff(ci)/2
alpha <- (1-conf.level)/2
if(width > sd){
warning("Width of confidence intervall > 2*sd, samplesize n=1 is ok for that case.")
return(1)
} else {
if(norm)
uniroot(f = function(n) sd/sqrt(n) * qnorm(p = 1-alpha) - width,
interval = interval, tol = tol)$root
else
uniroot(f = function(n) (qt(1-alpha, df=n-1) * sd / sqrt(n)) - width,
interval = interval, tol = tol)$root
}
}
MeanDiffCI <- function(x, ...){
UseMethod("MeanDiffCI")
}
MeanDiffCI.formula <- function (formula, data, subset, na.action, ...) {
# this is from t.test.formula
if (missing(formula) || (length(formula) != 3L) || (length(attr(terms(formula[-2L]),
"term.labels")) != 1L))
stop("'formula' missing or incorrect")
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, parent.frame())))
m$data <- as.data.frame(data)
m[[1L]] <- as.name("model.frame")
m$... <- NULL
mf <- eval(m, parent.frame())
DNAME <- paste(names(mf), collapse = " by ")
names(mf) <- NULL
response <- attr(attr(mf, "terms"), "response")
g <- factor(mf[[-response]])
if (nlevels(g) != 2L)
stop("grouping factor must have exactly 2 levels")
DATA <- setNames(split(mf[[response]], g), c("x", "y"))
y <- DoCall("MeanDiffCI", c(DATA, list(...)))
# y$data.name <- DNAME
# if (length(y$estimate) == 2L)
# names(y$estimate) <- paste("mean in group", levels(g))
y
}
MeanDiffCI.default <- function (x, y, method = c("classic", "norm","basic","stud","perc","bca"),
conf.level = 0.95, sides = c("two.sided","left","right"),
paired = FALSE, na.rm = FALSE, R=999, ...) {
if (na.rm) {
x <- na.omit(x)
y <- na.omit(y)
}
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
method <- match.arg(method, c("classic", "norm","basic","stud","perc","bca"))
if(method == "classic"){
a <- t.test(x, y, conf.level = conf.level, paired = paired)
if(paired)
res <- c(meandiff = mean(x - y), lwr.ci = a$conf.int[1], upr.ci = a$conf.int[2])
else
res <- c(meandiff = mean(x) - mean(y), lwr.ci = a$conf.int[1], upr.ci = a$conf.int[2])
} else {
diff.means <- function(d, f){
n <- nrow(d)
gp1 <- 1:table(as.numeric(d[,2]))[1]
m1 <- sum(d[gp1,1] * f[gp1])/sum(f[gp1])
m2 <- sum(d[-gp1,1] * f[-gp1])/sum(f[-gp1])
m1 - m2
}
m <- cbind(c(x,y), c(rep(1,length(x)), rep(2,length(y))))
if(paired)
boot.fun <- boot(x-y, function(d, i) mean(d[i]), R=R, stype="i")
else
boot.fun <- boot(m, diff.means, R=R, stype="f", strata = m[,2])
ci <- boot.ci(boot.fun, conf=conf.level, type=method)
if(method == "norm"){
res <- c(meandiff=boot.fun$t0, lwr.ci=ci[[4]][2], upr.ci=ci[[4]][3])
} else {
res <- c(meandiff=boot.fun$t0, lwr.ci=ci[[4]][4], upr.ci=ci[[4]][5])
}
}
if(sides=="left")
res[3] <- Inf
else if(sides=="right")
res[2] <- -Inf
return(res)
}
# CohenEffectSize <- function(x){
# (C) Antti Arppe 2007-2011
# E-mail: antti.arppe@helsinki.fi
# Cohen's Effect Size (1988)
# e0 <- matrix(,ctable.rows,ctable.cols)
# for(i in 1:ctable.rows)
# for(j in 1:ctable.cols)
# e0[i,j] <- sum.row[i]*sum.col[j]/N
# p0 <- e0/N
# p1 <- ctable/N
# effect.size <- sqrt(sum(((p1-p0)^2)/p0))
# noncentrality <- N*(effect.size^2)
# d.f=(ctable.rows-1)*(ctable.cols-1)
# beta <- pchisq(qchisq(alpha,df=d.f,lower.tail=FALSE),df=d.f,ncp=noncentrality)
# power <- 1-beta
# return(effect.size)
# }
.cohen_d_ci <- function (d, n = NULL, n2 = NULL, n1 = NULL, alpha = 0.05) {
# William Revelle in psych
d2t <- function (d, n = NULL, n2 = NULL, n1 = NULL) {
if (is.null(n1)) {
t <- d * sqrt(n)/2
} else if (is.null(n2)) {
t <- d * sqrt(n1)
} else {
t <- d/sqrt(1/n1 + 1/n2)
}
return(t)
}
t2d <- function (t, n = NULL, n2 = NULL, n1 = NULL) {
if (is.null(n1)) {
d <- 2 * t/sqrt(n)
} else {
if (is.null(n2)) {
d <- t/sqrt(n1)
} else {
d <- t * sqrt(1/n1 + 1/n2)
}
}
return(d)
}
t <- d2t(d = d, n = n, n2 = n2, n1 = n1)
tail <- 1 - alpha/2
ci <- matrix(NA, ncol = 3, nrow = length(d))
for (i in 1:length(d)) {
nmax <- max(c(n/2 + 1, n1 + 1, n1 + n2))
upper <- try(t2d(uniroot(function(x) {
suppressWarnings(pt(q = t[i], df = nmax - 2, ncp = x)) -
alpha/2
}, c(min(-5, -abs(t[i]) * 10), max(5, abs(t[i]) * 10)))$root,
n = n[i], n2 = n2[i], n1 = n1[i]), silent = TRUE)
if (inherits(upper, "try-error")) {
ci[i, 3] <- NA
}
else {
ci[i, 3] <- upper
}
ci[i, 2] <- d[i]
lower.ci <- try(t2d(uniroot(function(x) {
suppressWarnings(pt(q = t[i], df = nmax - 2, ncp = x)) -
tail
}, c(min(-5, -abs(t[i]) * 10), max(5, abs(t[i]) * 10)))$root,
n = n[i], n2 = n2[i], n1 = n1[i]), silent = TRUE)
if (inherits(lower.ci, "try-error")) {
ci[i, 1] <- NA
}
else {
ci[i, 1] <- lower.ci
}
}
colnames(ci) <- c("lower", "effect", "upper")
rownames(ci) <- names(d)
return(ci)
}
CohenD <- function(x, y=NULL, pooled = TRUE, correct = FALSE, conf.level = NA, na.rm = FALSE) {
if (na.rm) {
x <- na.omit(x)
if(!is.null(y)) y <- na.omit(y)
}
if(is.null(y)){ # one sample Cohen d
d <- mean(x) / sd(x)
n <- length(x)
if(!is.na(conf.level)){
# # reference: Smithson Confidence Intervals pp. 36:
# ci <- .nctCI(d / sqrt(n), df = n-1, conf = conf.level)
# res <- c(d=d, lwr.ci=ci[1]/sqrt(n), upr.ci=ci[3]/sqrt(n))
# changed to Revelle 2022-10-22:
ci <- .cohen_d_ci(d = d, n = n, alpha = 1-conf.level)
res <- c(d=d, lwr.ci=ci[1], upr.ci=ci[3])
} else {
res <- d
}
} else {
meanx <- mean(x)
meany <- mean(y)
# ssqx <- sum((x - meanx)^2)
# ssqy <- sum((y - meany)^2)
nx <- length(x)
ny <- length(y)
DF <- nx + ny - 2
d <- (meanx - meany)
if(pooled){
d <- d / sqrt(((nx - 1) * var(x) + (ny - 1) * var(y)) / DF)
}else{
d <- d / sd(c(x, y))
}
# if(unbiased) d <- d * gamma(DF/2)/(sqrt(DF/2) * gamma((DF - 1)/2))
if(correct){ # "Hedges's g"
# Hedges, L. V. & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando, FL: Academic Press.
d <- d * (1 - 3 / ( 4 * (nx + ny) - 9))
}
if(!is.na(conf.level)) {
# old:
# The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009)
## p 238
# ci <- d + c(-1, 1) * sqrt(((nx+ny) / (nx*ny) + .5 * d^2 / DF) * ((nx + ny)/DF)) * qt((1 - conf.level) / 2, DF)
# # supposed to be better, Smithson's version:
# ci <- .nctCI(d / sqrt(nx*ny/(nx+ny)), df = DF, conf = conf.level)
# res <- c(d=d, lwr.ci=ci[1]/sqrt(nx*ny/(nx+ny)), upr.ci=ci[3]/sqrt(nx*ny/(nx+ny)))
# changed to Revelle
ci <- .cohen_d_ci(d, n2 = nx, n1 = ny, alpha = 1-conf.level)
res <- c(d=d, lwr.ci=unname(ci[1]), upr.ci=unname(ci[3]))
} else {
res <- d
}
}
## Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159. Crow, E. L. (1991).
attr(res, "magnitude") <- c("negligible","small","medium","large")[findInterval(abs(d), c(0.2, 0.5, 0.8)) + 1]
return(res)
}
# find non-centrality parameter for the F-distribution
ncparamF <- function(type1, type2, nu1, nu2) {
# author Ali Baharev <ali.baharev at gmail.com>
# Returns the noncentrality parameter of the noncentral F distribution
# if probability of Type I and Type II error, degrees of freedom of the
# numerator and the denominator in the F test statistics are given.
.C("fpow", PACKAGE = "DescTools", as.double(type1), as.double(type2),
as.double(nu1), as.double(nu2), lambda=double(1))$lambda
}
.nctCI <- function(t, df, conf) {
alpha <- 1 - conf
probs <- c(alpha/2, 1 - alpha/2)
ncp <- suppressWarnings(optim(par = 1.1 * rep(t, 2), fn = function(x) {
p <- pt(q = t, df = df, ncp = x)
abs(max(p) - probs[2]) + abs(min(p) - probs[1])
}, control = list(abstol = 0.000000001)))
t_ncp <- unname(sort(ncp$par))
return(t_ncp)
}
# .nctCI <- function(tval.1, df, conf) {
#
# # Function for finding the upper and lower confidence limits for the noncentrality from noncentral t distributions.
# # Especially helpful when forming confidence intervals around the standardized effect size, Cohen's d.
#
# ###################################################################################################################
# # The following code was adapted from code written by Michael Smithson:
# # Australian National University, sometime around the early part of October, 2001
# # Adapted by Joe Rausch & Ken Kelley: University of Notre Dame, in January 2002.
# # Available at: JRausch@nd.edu & KKelley@nd.edu
# ###################################################################################################################
#
#
# # tval.1 is the observed t value, df is the degrees of freedom (group size need not be equal), and conf is simply 1 - alpha
#
# # Result <- matrix(NA,1,4)
# tval <- abs(tval.1)
#
#
# ############################This part Finds the Lower bound for the confidence interval###########################
# ulim <- 1 - (1-conf)/2
#
# # This first part finds a lower value from which to start.
# lc <- c(-tval,tval/2,tval)
# while(pt(tval, df, lc[1])<ulim) {
# lc <- c(lc[1]-tval,lc[1],lc[3])
# }
#
# # This next part finds the lower limit for the ncp.
# diff <- 1
# while(diff > .00000001) {
# if(pt(tval, df, lc[2]) <ulim)
# lc <- c(lc[1],(lc[1]+lc[2])/2,lc[2])
# else lc <- c(lc[2],(lc[2]+lc[3])/2,lc[3])
# diff <- abs(pt(tval,df,lc[2]) - ulim)
# ucdf <- pt(tval,df,lc[2])
# }
# res.1 <- ifelse(tval.1 >= 0,lc[2],-lc[2])
#
# ############################This part Finds the Upper bound for the confidence interval###########################
# llim <- (1-conf)/2
#
# # This first part finds an upper value from which to start.
# uc <- c(tval,1.5*tval,2*tval)
# while(pt(tval,df,uc[3])>llim) {
# uc <- c(uc[1],uc[3],uc[3]+tval)
# }
#
# # This next part finds the upper limit for the ncp.
# diff <- 1
# while(diff > .00000001) {
# if(pt(tval,df,uc[2])<llim)
# uc <- c(uc[1],(uc[1]+uc[2])/2,uc[2])
# else uc <- c(uc[2],(uc[2]+uc[3])/2,uc[3])
# diff <- abs(pt(tval,df,uc[2]) - llim)
# lcdf <- pt(tval,df,uc[2])
# }
# res <- ifelse(tval.1 >= 0,uc[2],-uc[2])
#
#
# #################################This part Compiles the results into a matrix#####################################
#
# return(c(lwr.ci=min(res, res.1), lprob=ucdf, upr.ci=max(res, res.1), uprob=lcdf))
#
# # Result[1,1] <- min(res,res.1)
# # Result[1,2] <- ucdf
# # Result[1,3] <- max(res,res.1)
# # Result[1,4] <- lcdf
# # dimnames(Result) <- list("Values", c("Lower.Limit", "Prob.Low.Limit", "Upper.Limit", "Prob.Up.Limit"))
# # Result
# }
CoefVar <- function (x, ...) {
UseMethod("CoefVar")
}
CoefVar.lm <- function (x, unbiased = FALSE, na.rm = FALSE, ...) {
# source: http://www.ats.ucla.edu/stat/mult_pkg/faq/general/coefficient_of_variation.htm
# In the modeling setting, the CV is calculated as the ratio of the root mean squared error (RMSE)
# to the mean of the dependent variable.
# root mean squared error
rmse <- sqrt(sum(x$residuals^2) / x$df.residual)
res <- rmse / mean(x$model[[1]], na.rm=na.rm)
# This is the same approach as in CoefVar.default, but it's not clear
# if it is correct in the environment of a model
n <- x$df.residual
if (unbiased) {
res <- res * ((1 - (1/(4 * (n - 1))) + (1/n) * res^2) +
(1/(2 * (n - 1)^2)))
}
# if (!is.na(conf.level)) {
# ci <- .nctCI(sqrt(n)/res, df = n - 1, conf = conf.level)
# res <- c(est = res, low.ci = unname(sqrt(n)/ci["upr.ci"]),
# upr.ci = unname(sqrt(n)/ci["lwr.ci"]))
# }
return(res)
}
# interface for lme???
# dependent variable in lme
# dv <- unname(nlme::getResponse(x))
# CoefVar.default <- function (x, weights=NULL, unbiased = FALSE, conf.level = NA, na.rm = FALSE, ...) {
#
# if(is.null(weights)){
# if(na.rm) x <- na.omit(x)
# res <- SD(x) / Mean(x)
# n <- length(x)
#
# }
# else {
# res <- SD(x, weights = weights) / Mean(x, weights = weights)
# n <- sum(weights)
#
# }
#
# if(unbiased) {
# res <- res * ((1 - (1/(4*(n-1))) + (1/n) * res^2)+(1/(2*(n-1)^2)))
# }
#
# if(!is.na(conf.level)){
# ci <- .nctCI(sqrt(n)/res, df = n-1, conf = conf.level)
# res <- c(est=res, low.ci= unname(sqrt(n)/ci["upr.ci"]), upr.ci= unname(sqrt(n)/ci["lwr.ci"]))
# }
#
# return(res)
#
# }
CoefVarCI <- function (K, n, conf.level = 0.95,
sides = c("two.sided", "left", "right"),
method = c("nct","vangel","mckay","verrill","naive")) {
# Description of confidence intervals
# https://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/coefvacl.htm
.iCoefVarCI <- Vectorize(function(K, n, conf.level=0.95,
sides = c("two.sided", "left", "right"),
method = c("vangel","mckay","verrill","nct","naive")) {
method <- match.arg(method)
sides <- match.arg(sides, choices = c("two.sided", "left", "right"),
several.ok = FALSE)
# double alpha in case of one-sided intervals in order to be able
# to generally calculate twosided intervals and select afterwards..
if (sides != "two.sided")
conf.level <- 1 - 2 * (1 - conf.level)
alpha <- 1 - conf.level
df <- n - 1
u1 <- qchisq(1-alpha/2, df)
u2 <- qchisq(alpha/2, df)
switch(method, verrill = {
CI.lower <- 0
CI.upper <- 1
}, vangel = {
CI.lower <- K / sqrt(((u1+2)/n - 1) * K^2 + u1/df)
CI.upper <- K / sqrt(((u2+2)/n - 1) * K^2 + u2/df)
}, mckay = {
CI.lower <- K / sqrt((u1/n - 1) * K^2 + u1/df)
CI.upper <- K / sqrt((u2/n - 1) * K^2 + u2/df)
}, nct = {
ci <- .nctCI(sqrt(n)/K, df = df, conf = conf.level)
CI.lower <- unname(sqrt(n)/ci[2])
CI.upper <- unname(sqrt(n)/ci[1])
}, naive = {
CI.lower <- K * sqrt(df / u1)
CI.upper <- K * sqrt(df / u2)
}
)
ci <- c(est = K,
lwr.ci = CI.lower, # max(0, CI.lower),
upr.ci = CI.upper) # min(1, CI.upper))
if (sides == "left")
ci[3] <- Inf
else if (sides == "right")
ci[2] <- -Inf
return(ci)
})
sides <- match.arg(sides)
method <- match.arg(method)
res <- t(.iCoefVarCI(K=K, n=n, method=method, sides=sides, conf.level = conf.level))
return(res)
}
CoefVar.default <- function (x, weights = NULL, unbiased = FALSE,
na.rm = FALSE, ...) {
if (is.null(weights)) {
if (na.rm)
x <- na.omit(x)
res <- SD(x)/Mean(x)
n <- length(x)
} else {
res <- SD(x, weights = weights)/Mean(x, weights = weights)
n <- sum(weights)
}
if (unbiased) {
res <- res * ((1 - (1/(4 * (n - 1))) + (1/n) * res^2) + (1/(2 * (n - 1)^2)))
}
return(res)
}
# aus agricolae: Variations Koeffizient aus aov objekt
#
# CoefVar.aov <- function(x){
# return(sqrt(sum(x$residual^2) / x$df.residual) / mean(x$fitted.values))
# }
CoefVar.aov <- function (x, unbiased = FALSE, na.rm = FALSE, ...) {
# source: http://www.ats.ucla.edu/stat/mult_pkg/faq/general/coefficient_of_variation.htm
# In the modeling setting, the CV is calculated as the ratio of the root mean squared error (RMSE)
# to the mean of the dependent variable.
# root mean squared error
rmse <- sqrt(sum(x$residuals^2) / x$df.residual)
res <- rmse / mean(x$model[[1]], na.rm=na.rm)
# This is the same approach as in CoefVar.default, but it's not clear
# if it is correct in the enviroment of a model
n <- x$df.residual
if (unbiased) {
res <- res * ((1 - (1/(4 * (n - 1))) + (1/n) * res^2) +
(1/(2 * (n - 1)^2)))
}
# if (!is.na(conf.level)) {
# ci <- .nctCI(sqrt(n)/res, df = n - 1, conf = conf.level)
# res <- c(est = res, low.ci = unname(sqrt(n)/ci["upr.ci"]),
# upr.ci = unname(sqrt(n)/ci["lwr.ci"]))
# }
return(res)
}
VarCI <- function (x, method = c("classic", "bonett", "norm", "basic","stud","perc","bca"),
conf.level = 0.95, sides = c("two.sided","left","right"), na.rm = FALSE, R=999) {
if (na.rm) x <- na.omit(x)
method <- match.arg(method, c("classic","bonett", "norm","basic","stud","perc","bca"))
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
if(method == "classic"){
df <- length(x) - 1
v <- var(x)
res <- c (var = v, lwr.ci = df * v/qchisq((1 - conf.level)/2, df, lower.tail = FALSE)
, upr.ci = df * v/qchisq((1 - conf.level)/2, df) )
} else if(method=="bonett") {
z <- qnorm(1-(1-conf.level)/2)
n <- length(x)
cc <- n/(n-z)
v <- var(x)
mtr <- mean(x, trim = 1/(2*(n-4)^0.5))
m <- mean(x)
gam4 <- n * sum((x-mtr)^4) / (sum((x-m)^2))^2
se <- cc * sqrt((gam4 - (n-3)/n)/(n-1))
lci <- exp(log(cc * v) - z*se)
uci <- exp(log(cc * v) + z*se)
res <- c(var=v, lwr.ci=lci, upr.ci=uci)
} else {
boot.fun <- boot(x, function(x, d) var(x[d], na.rm=na.rm), R=R)
ci <- boot.ci(boot.fun, conf=conf.level, type=method)
if(method == "norm"){
res <- c(var=boot.fun$t0, lwr.ci=ci[[4]][2], upr.ci=ci[[4]][3])
} else {
res <- c(var=boot.fun$t0, lwr.ci=ci[[4]][4], upr.ci=ci[[4]][5])
}
}
if(sides=="left")
res[3] <- Inf
else if(sides=="right")
res[2] <- 0
return(res)
}
## stats: Lorenz, <- & ineq ====
Lc <- function(x, ...)
UseMethod("Lc")
Lc.formula <- function(formula, data, subset, na.action, ...) {
# this is taken basically from wilcox.test.formula
if (missing(formula) || (length(formula) != 3L) || (length(attr(terms(formula[-2L]),
"term.labels")) != 1L))
stop("'formula' missing or incorrect")
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, parent.frame())))
m$data <- as.data.frame(data)
m[[1L]] <- as.name("model.frame")
m$... <- NULL
mf <- eval(m, parent.frame())
# mf$na.action <- substitute(na.action)
# DNAME <- paste(names(mf), collapse = " by ")
#
# DATA <- list(table(mf))
# do.call("Lc", c(DATA, list(...)))
drop <- TRUE
# mf <- model.frame(x, data)
x <- split(x = mf[,1], f = mf[,2], drop=drop, ...)
res <- lapply(x, FUN = "Lc", ...)
class(res) <- "Lclist"
return(res)
}
Lc.default <- function(x, n = rep(1, length(x)), na.rm = FALSE, ...) {
xx <- x
nn <- n
g <- Gini(x, n, na.rm=na.rm)
if(na.rm) x <- na.omit(x)
if (any(is.na(x)) || any(x < 0)) return(NA_real_)
k <- length(x)
o <- order(x)
x <- x[o]
n <- n[o]
x <- n*x
p <- cumsum(n)/sum(n)
L <- cumsum(x)/sum(x)
p <- c(0,p)
L <- c(0,L)
L2 <- L * mean(x)
Lc <- list(p, L, L2, g, xx, nn)
names(Lc) <- c("p", "L", "L.general", "Gini", "x", "n")
class(Lc) <- "Lc"
# no plot anymore, we have plot(lc) and Desc(lc, plotit=TRUE)
# if(plot) plot(Lc)
Lc
}
plot.Lc <- function(x, general=FALSE, lwd=2, type="l", xlab="p", ylab="L(p)",
main="Lorenz curve", las=1, pch=NA, ...) {
if(!general)
L <- x$L
else
L <- x$L.general
plot(x$p, L, type=type, main=main, lwd=lwd, xlab=xlab, ylab=ylab, xaxs="i",
yaxs="i", las=las, ...)
abline(0, max(L))
if(!is.na(pch)){
opar <- par(xpd=TRUE)
on.exit(par(opar))
points(x$p, L, pch=pch, ...)
}
}
lines.Lc <- function(x, general=FALSE, lwd=2, conf.level = NA, args.cband = NULL, ...) {
# Lc.boot.ci <- function(x, conf.level=0.95, n=1000){
#
# x <- rep(x$x, times=x$n)
# m <- matrix(sapply(1:n, function(i) sample(x, replace = TRUE)), nrow=length(x))
#
# lst <- apply(m, 2, Lc)
# list(x=c(lst[[1]]$p, rev(lst[[1]]$p)),
# y=c(apply(do.call(rbind, lapply(lst, "[[", "L")), 2, quantile, probs=(1-conf.level)/2),
# rev(apply(do.call(rbind, lapply(lst, "[[", "L")), 2, quantile, probs=1-(1-conf.level)/2)))
# )
# }
#
#
if(!general)
L <- x$L
else
L <- x$L.general
if (!(is.na(conf.level) || identical(args.cband, NA)) ) {
args.cband1 <- list(col = SetAlpha(DescToolsOptions("col")[1], 0.12), border = NA)
if (!is.null(args.cband))
args.cband1[names(args.cband)] <- args.cband
# ci <- Lc.boot.ci(x, conf.level=conf.level) # Vertrauensband
ci <- predict(object=x, conf.level=conf.level, general=general)
do.call("DrawBand", c(args.cband1, list(x=c(ci$p, rev(ci$p))),
list(y=c(ci$lci, rev(ci$uci)))))
}
lines(x$p, L, lwd=lwd, ...)
}
plot.Lclist <- function(x, col=1, lwd=2, lty=1, main = "Lorenz curve",
xlab="p", ylab="L(p)", ...){
# Recycle arguments
lgp <- Recycle(x=seq_along(x), col=col, lwd=lwd, lty=lty)
plot(x[[1]], col=lgp$col[1], lwd=lgp$lwd[1], lty=lgp$lty[1], main=main, xlab=xlab, ylab=ylab, ...)
for(i in 2L:length(x)){
lines(x[[i]], col=lgp$col[i], lwd=lgp$lwd[i], lty=lgp$lty[i])
}
}
predict.Lc <- function(object, newdata, conf.level=NA, general=FALSE, n=1000, ...){
confint.Lc <- function(object, conf.level = 0.95, general=FALSE, n=1000, ...){
x <- rep(object$x, times=object$n)
m <- replicate(n = n, sample(x, replace = TRUE))
lst <- apply(m, 2, Lc)
list(x=lst[[1]]$p,
lci=apply(do.call(rbind, lapply(lst, "[[", ifelse(general, "L.general", "L"))), 2, quantile, probs=(1-conf.level)/2),
uci=apply(do.call(rbind, lapply(lst, "[[", ifelse(general, "L.general", "L"))), 2, quantile, probs=1-(1-conf.level)/2)
)
}
if(!general)
L <- object$L
else
L <- object$L.general
if(missing(newdata)){
newdata <- object$p
res <- data.frame(p=object$p, L=L)
} else {
res <- do.call(data.frame, approx(x=object$p, y=L, xout=newdata))
colnames(res) <- c("p", "L")
}
if(!identical(conf.level, NA)){
ci <- confint.Lc(object, conf.level=conf.level, general=general, n=n)
lci <- approx(x=ci$x, y=ci$lci, xout=newdata)
uci <- approx(x=ci$x, y=ci$uci, xout=newdata)
res <- data.frame(res, lci=lci$y, uci=uci$y)
}
res
}
# Original Zeileis:
# Gini <- function(x)
# {
# n <- length(x)
# x <- sort(x)
# G <- sum(x * 1:n)
# G <- 2*G/(n*sum(x))
# G - 1 - (1/n)
# }
# other:
# http://rss.acs.unt.edu/Rdoc/library/reldist/html/gini.html
# http://finzi.psych.upenn.edu/R/library/dplR/html/gini.coef.html
# Gini <- function(x, n = rep(1, length(x)), unbiased = TRUE, conf.level = NA, R = 1000, type = "bca", na.rm = FALSE) {
#
# # cast to numeric, as else sum(x * 1:n) might overflow for integers
# # http://stackoverflow.com/questions/39579029/integer-overflow-error-using-gini-function-of-package-desctools
# x <- as.numeric(x)
#
# x <- rep(x, n) # same handling as Lc
# if(na.rm) x <- na.omit(x)
# if (any(is.na(x)) || any(x < 0)) return(NA_real_)
#
# i.gini <- function (x, unbiased = TRUE){
# n <- length(x)
# x <- sort(x)
#
# res <- 2 * sum(x * 1:n) / (n*sum(x)) - 1 - (1/n)
# if(unbiased) res <- n / (n - 1) * res
#
# # limit Gini to 0 here, if negative values appear, which is the case with
# # Gini( c(10,10,10))
# return( pmax(0, res))
#
# # other guy out there:
# # N <- if (unbiased) n * (n - 1) else n * n
# # dsum <- drop(crossprod(2 * 1:n - n - 1, x))
# # dsum / (mean(x) * N)
# # is this slower, than above implementation??
# }
#
# if(is.na(conf.level)){
# res <- i.gini(x, unbiased = unbiased)
#
# } else {
# # adjusted bootstrap percentile (BCa) interval
# boot.gini <- boot(x, function(x, d) i.gini(x[d], unbiased = unbiased), R=R)
# ci <- boot.ci(boot.gini, conf=conf.level, type=type)
# res <- c(gini=boot.gini$t0, lwr.ci=ci[[4]][4], upr.ci=ci[[4]][5])
# }
#
# return(res)
#
# }
# recoded for better support weights 2022-09-14
Gini <- function(x, weights=NULL, unbiased=TRUE,
conf.level = NA, R = 10000, type = "bca", na.rm=FALSE) {
# https://core.ac.uk/download/pdf/41339501.pdf
if (is.null(weights)) {
weights <- rep(1, length(x))
}
if (na.rm){
na <- (is.na(x) | is.na(weights))
x <- x[!na]
weights <- weights[!na]
}
if (any(is.na(x)) || any(x < 0))
return(NA_real_)
i.gini <- function(x, w, unbiased=FALSE) {
w <- w/sum(w)
x <- x[id <- order(x)]
w <- w[id]
f.hat <- w / 2 + c(0, head(cumsum(w), -1))
wm <- Mean(x, w)
res <- 2 / wm * sum(w * (x - wm) * (f.hat - Mean(f.hat, w)))
if(unbiased)
res <- res * 1/(1 - sum(w^2))
return(res)
}
if (is.na(conf.level)) {
res <- i.gini(x, weights, unbiased = unbiased)
} else {
boot.gini <- boot(data = x,
statistic = function(z, i, u, unbiased)
i.gini(x = z[i], w = u[i], unbiased = unbiased),
R=R, u=weights, unbiased=unbiased)
ci <- boot.ci(boot.gini, conf = conf.level, type = type)
res <- c(gini = boot.gini$t0, lwr.ci = ci[[4]][4], upr.ci = ci[[4]][5])
}
return(res)
}
GiniSimpson <- function(x, na.rm = FALSE) {
# referenz: Sachs, Angewandte Statistik, S. 57
# example:
# x <- as.table(c(69,17,7,62))
# rownames(x) <- c("A","B","AB","0")
# GiniSimpson(x)
if(!is.factor(x)){
warning("x is not a factor!")
return(NA)
}
if(na.rm) x <- na.omit(x)
ptab <- prop.table(table(x))
return(sum(ptab*(1-ptab)))
}
GiniDeltas <- function (x, na.rm = FALSE) {
# Deltas (2003, DOI:10.1162/rest.2003.85.1.226).
if (!is.factor(x)) {
warning("x is not a factor!")
return(NA)
}
if (na.rm)
x <- na.omit(x)
p <- prop.table(table(x))
sum(p * (1 - p)) * length(p)/(length(p) - 1)
}
HunterGaston <- function(x, na.rm = FALSE){
# Hunter-Gaston index (Hunter & Gaston, 1988, DOI:10.1128/jcm.26.11.2465-2466.1988)
# Credits to Wim Bernasco
# https://github.com/AndriSignorell/DescTools/issues/120
# see: vegan::simpson.unb(BCI)
if (is.factor(x) | is.character(x)) {
if (na.rm)
x <- na.omit(x)
tt <- table(x)
} else {
tt <- x
}
sum(tt * (tt - 1)) / (sum(tt) * (sum(tt) - 1))
# shouldn't this be:
# 1 - sum(tt * (tt - 1)) / (sum(tt) * (sum(tt) - 1))
# ???
}
Atkinson <- function(x, n = rep(1, length(x)), parameter = 0.5, na.rm = FALSE) {
x <- rep(x, n) # same handling as Lc and Gini
if(na.rm) x <- na.omit(x)
if (any(is.na(x)) || any(x < 0)) return(NA_real_)
if(is.null(parameter)) parameter <- 0.5
if(parameter==1)
A <- 1 - (exp(mean(log(x)))/mean(x))
else
{
x <- (x/mean(x))^(1-parameter)
A <- 1 - mean(x)^(1/(1-parameter))
}
A
}
Herfindahl <- function(x, n = rep(1, length(x)), parameter=1, na.rm = FALSE) {
x <- rep(x, n) # same handling as Lc and Gini
if(na.rm) x <- na.omit(x)
if (any(is.na(x)) || any(x < 0)) return(NA_real_)
if(is.null(parameter))
m <- 1
else
m <- parameter
Herf <- x/sum(x)
Herf <- Herf^(m+1)
Herf <- sum(Herf)^(1/m)
Herf
}
Rosenbluth <- function(x, n = rep(1, length(x)), na.rm = FALSE) {
x <- rep(x, n) # same handling as Lc and Gini
if(na.rm) x <- na.omit(x)
if (any(is.na(x)) || any(x < 0)) return(NA_real_)
n <- length(x)
x <- sort(x)
HT <- (n:1)*x
HT <- 2*sum(HT/sum(x))
HT <- 1/(HT-1)
HT
}
###
## stats: assocs etc. ====
CutAge <- function(x, from=0, to=90, by=10, right=FALSE, ordered_result=TRUE, ...){
cut(x, breaks = c(seq(from, to, by), Inf),
right=right, ordered_result = ordered_result, ...)
}
CutQ <- function(x, breaks=quantile(x, seq(0, 1, by=0.25), na.rm=TRUE),
labels=NULL, na.rm = FALSE, ...){
# old version:
# cut(x, breaks=probsile(x, breaks=probs, na.rm = na.rm), include.lowest=TRUE, labels=labels)
# $Id: probscut.R 1431 2010-04-28 17:23:08Z ggrothendieck2 $
# from gtools
if(na.rm) x <- na.omit(x)
if(length(breaks)==1 && IsWhole(breaks))
breaks <- quantile(x, seq(0, 1, by = 1/breaks), na.rm = TRUE)
if(is.null(labels)) labels <- gettextf("Q%s", 1:(length(breaks)-1))
# probs <- quantile(x, probs)
dups <- duplicated(breaks)
if(any(dups)) {
flag <- x %in% unique(breaks[dups])
retval <- ifelse(flag, paste("[", as.character(x), "]", sep=''), NA)
uniqs <- unique(breaks)
# move cut points over a bit...
reposition <- function(cut) {
flag <- x>=cut
if(sum(flag)==0)
return(cut)
else
return(min(x[flag]))
}
newprobs <- sapply(uniqs, reposition)
retval[!flag] <- as.character(cut(x[!flag], breaks=newprobs, include.lowest=TRUE,...))
levs <- unique(retval[order(x)]) # ensure factor levels are
# properly ordered
retval <- factor(retval, levels=levs)
## determine open/closed interval ends
mkpairs <- function(x) # make table of lower, upper
sapply(x,
function(y) if(length(y)==2) y[c(2,2)] else y[2:3]
)
pairs <- mkpairs(strsplit(levs, '[^0-9+\\.\\-]+'))
rownames(pairs) <- c("lower.bound","upper.bound")
colnames(pairs) <- levs
closed.lower <- rep(FALSE, ncol(pairs)) # default lower is open
closed.upper <- rep(TRUE, ncol(pairs)) # default upper is closed
closed.lower[1] <- TRUE # lowest interval is always closed
for(i in 2:ncol(pairs)) # open lower interval if above singlet
if(pairs[1,i]==pairs[1,i-1] && pairs[1,i]==pairs[2,i-1])
closed.lower[i] <- FALSE
for(i in 1:(ncol(pairs)-1)) # open upper interval if below singlet
if(pairs[2,i]==pairs[1,i+1] && pairs[2,i]==pairs[2,i+1])
closed.upper[i] <- FALSE
levs <- ifelse(pairs[1,]==pairs[2,],
pairs[1,],
paste(ifelse(closed.lower,"[","("),
pairs[1,],
",",
pairs[2,],
ifelse(closed.upper,"]",")"),
sep='')
)
levels(retval) <- levs
} else
retval <- cut( x, breaks, include.lowest=TRUE, labels=labels, ... )
return(retval)
}
# Phi-Koeff
Phi <- function (x, y = NULL, ...) {
if(!is.null(y)) x <- table(x, y, ...)
# when computing phi, note that Yates' correction to chi-square must not be used.
as.numeric( sqrt( suppressWarnings(chisq.test(x, correct=FALSE)$statistic) / sum(x) ) )
# should we implement: ??
# following http://technology.msb.edu/old/training/statistics/sas/books/stat/chap26/sect19.htm#idxfrq0371
# (Liebetrau 1983)
# this makes phi -1 < phi < 1 for 2x2 tables (same for CramerV)
# (prod(diag(x)) - prod(diag(Rev(x, 2)))) / sqrt(prod(colSums(x), rowSums(x)))
}
# Kontingenz-Koeffizient
ContCoef <- function(x, y = NULL, correct = FALSE, ...) {
if(!is.null(y)) x <- table(x, y, ...)
chisq <- suppressWarnings(chisq.test(x, correct = FALSE)$statistic)
cc <- as.numeric( sqrt( chisq / ( chisq + sum(x)) ))
if(correct) { # Sakoda's adjusted Pearson's C
k <- min(nrow(x),ncol(x))
cc <- cc/sqrt((k-1)/k)
}
return(cc)
}
.ncchisqCI <- function(chisq, df, conf) {
alpha <- 1 - conf
probs <- c(alpha/2, 1 - alpha/2)
ncp <- suppressWarnings(optim(par = 1.1 * rep(chisq, 2), fn = function(x) {
p <- pchisq(q = chisq, df = df, ncp = x)
abs(max(p) - probs[2]) + abs(min(p) - probs[1])
}, control = list(abstol = 0.000000001)))
chisq_ncp <- unname(sort(ncp$par))
return(chisq_ncp)
}
CramerV <- function(x, y = NULL, conf.level = NA,
method = c("ncchisq", "ncchisqadj", "fisher", "fisheradj"),
correct=FALSE, ...){
if(!is.null(y)) x <- table(x, y, ...)
# CIs and power for the noncentral chi-sq noncentrality parameter (ncp):
# The function lochi computes the lower CI limit and hichi computes the upper limit.
# Both functions take 3 arguments: observed chi-sq, df, and confidence level.
# author: Michael Smithson
# http://psychology3.anu.edu.au/people/smithson/details/CIstuff/Splusnonc.pdf
# see also: MBESS::conf.limits.nc.chisq, Ken Kelly
lochi <- function(chival, df, conf) {
# we don't have lochi for chival==0
# optimize would report minval = maxval
if(chival==0) return(NA)
ulim <- 1 - (1-conf)/2
# This first part finds a lower value from which to start.
lc <- c(.001, chival/2, chival)
while(pchisq(chival, df, lc[1]) < ulim) {
if(pchisq(chival, df) < ulim)
return(c(0, pchisq(chival, df)))
lc <- c(lc[1]/4, lc[1], lc[3])
}
# This next part finds the lower limit for the ncp.
diff <- 1
while(diff > .00001) {
if(pchisq(chival, df, lc[2]) < ulim)
lc <- c(lc[1],(lc[1]+lc[2])/2, lc[2])
else lc <- c(lc[2], (lc[2]+lc[3])/2, lc[3])
diff <- abs(pchisq(chival, df, lc[2]) - ulim)
ucdf <- pchisq(chival, df, lc[2])
}
c(lc[2], ucdf)
}
hichi <- function(chival,df,conf) {
# we don't have hichi for chival==0
if(chival==0) return(NA)
# This first part finds upper and lower startinig values.
uc <- c(chival, 2*chival, 3*chival)
llim <- (1-conf)/2
while(pchisq(chival, df, uc[1]) < llim) {
uc <- c(uc[1]/4,uc[1],uc[3])
}
while(pchisq(chival,df,uc[3])>llim) {
uc <- c(uc[1],uc[3],uc[3]+chival)
}
# This next part finds the upper limit for the ncp.
diff <- 1
while(diff > .00001) {
if(pchisq(chival, df, uc[2]) < llim)
uc <- c(uc[1], (uc[1] + uc[2]) / 2, uc[2])
else uc <- c(uc[2], (uc[2] + uc[3]) / 2, uc[3])
diff <- abs(pchisq(chival, df, uc[2]) - llim)
lcdf <- pchisq(chival, df, uc[2])
}
c(uc[2], lcdf)
}
# Remark Andri 18.12.2014:
# lochi and hichi could be replaced with:
# optimize(function(x) abs(pchisq(chival, DF, x) - (1-(1-conf.level)/2)), c(0, chival))
# optimize(function(x) abs(pchisq(chival, DF, x) - (1-conf.level)/2), c(0, 3*chival))
#
# ... which would run ~ 25% faster and be more exact
# what can go wrong while calculating chisq.stat?
# we don't need test results here, so we suppress those warnings
chisq.hat <- suppressWarnings(chisq.test(x, correct = FALSE)$statistic)
df <- prod(dim(x)-1)
n <- sum(x)
if(correct){
# Bergsma, W, A bias-correction for Cramer's V and Tschuprow's T
# September 2013Journal of the Korean Statistical Society 42(3)
# DOI: 10.1016/j.jkss.2012.10.002
phi.hat <- chisq.hat / n
v <- as.numeric(sqrt(max(0, phi.hat - df/(n-1)) /
(min(sapply(dim(x), function(i) i - 1 / (n-1) * (i-1)^2) - 1))))
} else {
v <- as.numeric(sqrt(chisq.hat/(n * (min(dim(x)) - 1))))
}
if (is.na(conf.level)) {
res <- v
} else {
switch(match.arg(method),
ncchisq={
ci <- c(lochi(chisq.hat, df, conf.level)[1], hichi(chisq.hat, df, conf.level)[1])
# corrected by michael smithson, 17.5.2014:
# ci <- unname(sqrt( (ci + df) / (sum(x) * (min(dim(x)) - 1)) ))
ci <- unname(sqrt( (ci) / (n * (min(dim(x)) - 1)) ))
},
ncchisqadj={
ci <- c(lochi(chisq.hat, df, conf.level)[1] + df, hichi(chisq.hat, df, conf.level)[1] + df)
# corrected by michael smithson, 17.5.2014:
# ci <- unname(sqrt( (ci + df) / (sum(x) * (min(dim(x)) - 1)) ))
ci <- unname(sqrt( (ci) / (n * (min(dim(x)) - 1)) ))
},
fisher={
se <- 1 / sqrt(n-3) * qnorm(1-(1-conf.level)/2)
ci <- tanh(atanh(v) + c(-se, se))
},
fisheradj={
se <- 1 / sqrt(n-3) * qnorm(1-(1-conf.level)/2)
# bias correction
adj <- 0.5 * v / (n-1)
ci <- tanh(atanh(v) + c(-se, se) + adj)
})
# "Cram\u00E9r's association coefficient"
res <- c("Cramer V"=v, lwr.ci=max(0, ci[1]), upr.ci=min(1, ci[2]))
}
return(res)
}
YuleQ <- function(x, y = NULL, ...){
if(!is.null(y)) x <- table(x, y, ...)
# allow only 2x2 tables
stopifnot(prod(dim(x)) == 4 || length(x) == 4)
a <- x[1,1]
b <- x[1,2]
c <- x[2,1]
d <- x[2,2]
return((a*d- b*c)/(a*d + b*c)) #Yule Q
}
YuleY <- function(x, y = NULL, ...){
if(!is.null(y)) x <- table(x, y, ...)
# allow only 2x2 tables
stopifnot(prod(dim(x)) == 4 || length(x) == 4)
a <- x[1,1]
b <- x[1,2]
c <- x[2,1]
d <- x[2,2]
return((sqrt(a*d) - sqrt(b*c))/(sqrt(a*d)+sqrt(b*c))) # YuleY
}
TschuprowT <- function(x, y = NULL, correct = FALSE, ...){
if(!is.null(y)) x <- table(x, y, ...)
# Tschuprow, A. A. (1939) Principles of the Mathematical Theory of Correlation; translated by M. Kantorowitsch. W. Hodge & Co.
# http://en.wikipedia.org/wiki/Tschuprow's_T
# Hartung S. 451
# what can go wrong while calculating chisq.stat?
# we don't need test results here, so we suppress those warnings
chisq.hat <- suppressWarnings(chisq.test(x, correct = FALSE)$statistic)
n <- sum(x)
df <- prod(dim(x)-1)
if(correct) {
# Bergsma, W, A bias-correction for Cramer's V and Tschuprow's T
# September 2013Journal of the Korean Statistical Society 42(3)
# DOI: 10.1016/j.jkss.2012.10.002
# see also CramerV
phi.hat <- chisq.hat / n
as.numeric(sqrt(max(0, phi.hat - df/(n-1)) /
(sqrt(prod(sapply(dim(x), function(i) i - 1 / (n-1) * (i-1)^2) - 1)))))
} else {
as.numeric( sqrt(chisq.hat/(n * sqrt(df))))
}
}
# based on Kappa from library(vcd)
# author: David Meyer
# see also: kappa in library(psych)
CohenKappa <- function (x, y = NULL,
weights = c("Unweighted", "Equal-Spacing", "Fleiss-Cohen"),
conf.level = NA, ...) {
if (is.character(weights))
weights <- match.arg(weights)
if (!is.null(y)) {
# we can not ensure a reliable weighted kappa for 2 factors with different levels
# so refuse trying it... (unweighted is no problem)
if (!identical(weights, "Unweighted"))
stop("Vector interface for weighted Kappa is not supported. Provide confusion matrix.")
# x and y must have the same levels in order to build a symmetric confusion matrix
x <- factor(x)
y <- factor(y)
lvl <- unique(c(levels(x), levels(y)))
x <- factor(x, levels = lvl)
y <- factor(y, levels = lvl)
x <- table(x, y, ...)
} else {
d <- dim(x)
if (d[1L] != d[2L])
stop("x must be square matrix if provided as confusion matrix")
}
d <- diag(x)
n <- sum(x)
nc <- ncol(x)
colFreqs <- colSums(x)/n
rowFreqs <- rowSums(x)/n
kappa <- function(po, pc) {
(po - pc)/(1 - pc)
}
std <- function(p, pc, k, W = diag(1, ncol = nc, nrow = nc)) {
sqrt((sum(p * sweep(sweep(W, 1, W %*% colSums(p) * (1 - k)),
2, W %*% rowSums(p) * (1 - k))^2) -
(k - pc * (1 - k))^2) / crossprod(1 - pc)/n)
}
if(identical(weights, "Unweighted")) {
po <- sum(d)/n
pc <- as.vector(crossprod(colFreqs, rowFreqs))
k <- kappa(po, pc)
s <- as.vector(std(x/n, pc, k))
} else {
# some kind of weights defined
W <- if (is.matrix(weights))
weights
else if (weights == "Equal-Spacing")
1 - abs(outer(1:nc, 1:nc, "-"))/(nc - 1)
else # weights == "Fleiss-Cohen"
1 - (abs(outer(1:nc, 1:nc, "-"))/(nc - 1))^2
po <- sum(W * x)/n
pc <- sum(W * colFreqs %o% rowFreqs)
k <- kappa(po, pc)
s <- as.vector(std(x/n, pc, k, W))
}
if (is.na(conf.level)) {
res <- k
} else {
ci <- k + c(1, -1) * qnorm((1 - conf.level)/2) * s
res <- c(kappa = k, lwr.ci = ci[1], upr.ci = ci[2])
}
return(res)
}
# KappaTest <- function(x, weights = c("Equal-Spacing", "Fleiss-Cohen"), conf.level = NA) {
# to do, idea is to implement a Kappa test for H0: kappa = 0 as in
# http://support.sas.com/documentation/cdl/en/statugfreq/63124/PDF/default/statugfreq.pdf, pp. 1687
# print( "still to do...." )
# }
KappaM <- function(x, method = c("Fleiss", "Conger", "Light"), conf.level = NA) {
# ratings <- as.matrix(na.omit(x))
#
# ns <- nrow(ratings)
# nr <- ncol(ratings)
#
# # Build table
# lev <- levels(as.factor(ratings))
#
# for (i in 1:ns) {
# frow <- factor(ratings[i,],levels=lev)
#
# if (i==1)
# ttab <- as.numeric(table(frow))
# else
# ttab <- rbind(ttab, as.numeric(table(frow)))
# }
#
# ttab <- matrix(ttab, nrow=ns)
# we have not factors for matrices, but we need factors below...
if(is.matrix(x))
x <- as.data.frame(x)
x <- na.omit(x)
ns <- nrow(x)
nr <- ncol(x)
# find all levels in the data (data.frame)
lev <- levels(factor(unlist(x)))
# apply the same levels to all variables and switch to integer matrix
xx <- do.call(cbind, lapply(x, factor, levels=lev))
ttab <- apply(Abind(lapply(as.data.frame(xx), function(z) Dummy(z, method="full", levels=seq_along(lev))), along = 3),
c(1,2), sum)
agreeP <- sum((rowSums(ttab^2)-nr)/(nr*(nr-1))/ns)
switch( match.arg(method, choices= c("Fleiss", "Conger", "Light"))
, "Fleiss" = {
chanceP <- sum(colSums(ttab)^2)/(ns*nr)^2
value <- (agreeP - chanceP)/(1 - chanceP)
pj <- colSums(ttab)/(ns*nr)
qj <- 1-pj
varkappa <- (2/(sum(pj*qj)^2*(ns*nr*(nr-1))))*(sum(pj*qj)^2-sum(pj*qj*(qj-pj)))
SEkappa <- sqrt(varkappa)
ci <- value + c(1,-1) * qnorm((1-conf.level)/2) * SEkappa
}
, "Conger" = {
# for (i in 1:nr) {
# rcol <- factor(x[,i],levels=lev)
#
# if (i==1)
# rtab <- as.numeric(table(rcol))
# else
# rtab <- rbind(rtab, as.numeric(table(rcol)))
# }
rtab <- apply(Abind(lapply(as.data.frame(t(xx)), function(z) Dummy(z, method="full", levels=seq_along(lev))), along = 3),
c(1,2), sum)
rtab <- rtab/ns
chanceP <- sum(colSums(ttab)^2)/(ns*nr)^2 - sum(apply(rtab, 2, var)*(nr-1)/nr)/(nr-1)
value <- (agreeP - chanceP)/(1 - chanceP)
# we have not SE for exact Kappa value
ci <- c(NA, NA)
}
, "Light" = {
m <- DescTools::PairApply(x, DescTools::CohenKappa, symmetric=TRUE)
value <- mean(m[upper.tri(m)])
levlen <- length(lev)
for (nri in 1:(nr - 1)) for (nrj in (nri + 1):nr) {
for (i in 1:levlen) for (j in 1:levlen) {
if (i != j) {
r1i <- sum(x[, nri] == lev[i])
r2j <- sum(x[, nrj] == lev[j])
if (!exists("dis"))
dis <- r1i * r2j
else dis <- c(dis, r1i * r2j)
}
}
if (!exists("disrater"))
disrater <- sum(dis)
else disrater <- c(disrater, sum(dis))
rm(dis)
}
B <- length(disrater) * prod(disrater)
chanceP <- 1 - B/ns^(choose(nr, 2) * 2)
varkappa <- chanceP/(ns * (1 - chanceP))
SEkappa <- sqrt(varkappa)
ci <- value + c(1,-1) * qnorm((1-conf.level)/2) * SEkappa
}
)
if (is.na(conf.level)) {
res <- value
} else {
res <- c("kappa"=value, lwr.ci=ci[1], upr.ci=ci[2])
}
return(res)
}
Agree <- function(x, tolerance = 0, na.rm = FALSE) {
x <- as.matrix(x)
if(na.rm) x <- na.omit(x)
if(anyNA(x)) return(NA)
ns <- nrow(x)
nr <- ncol(x)
if (is.numeric(x)) {
rangetab <- apply(x, 1, max) - apply(x, 1, min)
coeff <- sum(rangetab <= tolerance)/ns
} else {
rangetab <- as.numeric(sapply(apply(x, 1, table), length))
coeff <- (sum(rangetab == 1)/ns)
tolerance <- 0
}
rval <- coeff
attr(rval, c("subjects")) <- ns
attr(rval, c("raters")) <- nr
return(rval)
}
# ICC(ratings)
# ICC_(ratings, type="ICC3", conf.level=0.95)
# ICC_(ratings, type="all", conf.level=0.95)
ICC <- function(x, type=c("all", "ICC1","ICC2","ICC3","ICC1k","ICC2k","ICC3k"), conf.level = NA, na.rm = FALSE) {
ratings <- as.matrix(x)
if(na.rm) ratings <- na.omit(ratings)
ns <- nrow(ratings)
nr <- ncol(ratings)
x.s <- stack(data.frame(ratings))
x.df <- data.frame(x.s, subs = rep(paste("S", 1:ns, sep = ""), nr))
s.aov <- summary(aov(values ~ subs + ind, data=x.df))
stats <- matrix(unlist(s.aov), ncol=3, byrow=TRUE)
MSB <- stats[3,1]
MSW <- (stats[2,2] + stats[2,3])/(stats[1,2] + stats[1,3])
MSJ <- stats[3,2]
MSE <- stats[3,3]
ICC1 <- (MSB- MSW)/(MSB+ (nr-1)*MSW)
ICC2 <- (MSB- MSE)/(MSB + (nr-1)*MSE + nr*(MSJ-MSE)/ns)
ICC3 <- (MSB - MSE)/(MSB+ (nr-1)*MSE)
ICC12 <- (MSB-MSW)/(MSB)
ICC22 <- (MSB- MSE)/(MSB +(MSJ-MSE)/ns)
ICC32 <- (MSB-MSE)/MSB
#find the various F values from Shrout and Fleiss
F11 <- MSB/MSW
df11n <- ns-1
df11d <- ns*(nr-1)
p11 <- 1 - pf(F11, df11n, df11d)
F21 <- MSB/MSE
df21n <- ns-1
df21d <- (ns-1)*(nr-1)
p21 <- 1-pf(F21, df21n, df21d)
F31 <- F21
# results <- t(results)
results <- data.frame(matrix(NA, ncol=8, nrow=6))
colnames(results ) <- c("type", "est","F-val","df1","df2","p-val","lwr.ci","upr.ci")
rownames(results) <- c("Single_raters_absolute","Single_random_raters","Single_fixed_raters", "Average_raters_absolute","Average_random_raters","Average_fixed_raters")
results[,1] = c("ICC1","ICC2","ICC3","ICC1k","ICC2k","ICC3k")
results[,2] = c(ICC1, ICC2, ICC3, ICC12, ICC22, ICC32)
results[1,3] <- results[4,3] <- F11
results[2,3] <- F21
results[3,3] <- results[6,3] <- results[5,3] <- F31 <- F21
results[5,3] <- F21
results[1,4] <- results[4,4] <- df11n
results[1,5] <- results[4,5] <- df11d
results[1,6] <- results[4,6] <- p11
results[2,4] <- results[3,4] <- results[5,4] <- results[6,4] <- df21n
results[2,5] <- results[3,5] <- results[5,5] <- results[6,5] <- df21d
results[2,6] <- results[5,6] <- results[3,6] <- results[6,6] <- p21
#now find confidence limits
#first, the easy ones
alpha <- 1 - conf.level
F1L <- F11 / qf(1-alpha/2, df11n, df11d)
F1U <- F11 * qf(1-alpha/2, df11d, df11n)
L1 <- (F1L-1) / (F1L + (nr - 1))
U1 <- (F1U -1) / (F1U + nr - 1)
F3L <- F31 / qf(1-alpha/2, df21n, df21d)
F3U <- F31 * qf(1-alpha/2, df21d, df21n)
results[1,7] <- L1
results[1,8] <- U1
results[3,7] <- (F3L-1)/(F3L+nr-1)
results[3,8] <- (F3U-1)/(F3U+nr-1)
results[4,7] <- 1- 1/F1L
results[4,8] <- 1- 1/F1U
results[6,7] <- 1- 1/F3L
results[6,8] <- 1 - 1/F3U
#the hard one is case 2
Fj <- MSJ/MSE
vn <- (nr-1)*(ns-1)* ( (nr*ICC2*Fj+ns*(1+(nr-1)*ICC2) - nr*ICC2))^2
vd <- (ns-1)*nr^2 * ICC2^2 * Fj^2 + (ns *(1 + (nr-1)*ICC2) - nr*ICC2)^2
v <- vn/vd
F3U <- qf(1-alpha/2,ns-1,v)
F3L <- qf(1-alpha/2,v,ns-1)
L3 <- ns *(MSB- F3U*MSE)/(F3U*(nr * MSJ + (nr*ns-nr-ns) * MSE)+ ns*MSB)
results[2, 7] <- L3
U3 <- ns *(F3L * MSB - MSE)/(nr * MSJ + (nr * ns - nr - ns)*MSE + ns * F3L * MSB)
results[2, 8] <- U3
L3k <- L3 * nr/(1+ L3*(nr-1))
U3k <- U3 * nr/(1+ U3*(nr-1))
results[5, 7] <- L3k
results[5, 8] <- U3k
#clean up the output
results[,2:8] <- results[,2:8]
type <- match.arg(type, c("all", "ICC1","ICC2","ICC3","ICC1k","ICC2k","ICC3k"))
switch(type
, all={res <- list(results=results, summary=s.aov, stats=stats, MSW=MSW, ns=ns, nr=nr)
class(res) <- "ICC"
}
, ICC1={idx <- 1}
, ICC2={idx <- 2}
, ICC3={idx <- 3}
, ICC1k={idx <- 4}
, ICC2k={idx <- 5}
, ICC3k={idx <- 6}
)
if(type!="all"){
if(is.na(conf.level)){
res <- results[idx, c(2)][,]
} else {
res <- unlist(results[idx, c(2, 7:8)])
names(res) <- c(type,"lwr.ci","upr.ci")
}
}
return(res)
}
print.ICC <- function(x, digits = 3, ...){
cat("\nIntraclass correlation coefficients \n")
print(x$results, digits=digits)
cat("\n Number of subjects =", x$ns, " Number of raters =", x$nr, "\n")
}
# implementing Omega might be wise
# boostrap CI could be integrated in function instead on examples of help
CronbachAlpha <- function(x, conf.level = NA, cond = FALSE, na.rm = FALSE){
i.CronbachAlpha <- function(x, conf.level = NA){
nc <- ncol(x)
colVars <- apply(x, 2, var)
total <- var(rowSums(x))
res <- (total - sum(colVars)) / total * (nc/(nc-1))
if (!is.na(conf.level)) {
N <- length(x)
ci <- 1 - (1-res) * qf( c(1-(1-conf.level)/2, (1-conf.level)/2), N-1, (nc-1)*(N-1))
res <- c("Cronbach Alpha"=res, lwr.ci=ci[1], upr.ci=ci[2])
}
return(res)
}
x <- as.matrix(x)
if(na.rm) x <- na.omit(x)
res <- i.CronbachAlpha(x = x, conf.level = conf.level)
if(cond) {
condCronbachAlpha <- list()
n <- ncol(x)
if(n > 2) { # can't calculate conditional with only 2 items
for(i in 1:n){
condCronbachAlpha[[i]] <- i.CronbachAlpha(x[,-i], conf.level = conf.level)
}
condCronbachAlpha <- data.frame(Item = 1:n, do.call("rbind", condCronbachAlpha))
colnames(condCronbachAlpha)[2] <- "Cronbach Alpha"
}
res <- list(unconditional=res, condCronbachAlpha = condCronbachAlpha)
}
return(res)
}
KendallW <- function(x, correct=FALSE, test=FALSE, na.rm = FALSE) {
# see also old Jim Lemon function kendall.w
# other solution: library(irr); kendall(ratings, correct = TRUE)
# http://www.real-statistics.com/reliability/kendalls-w/
dname <- deparse(substitute(x))
ratings <- as.matrix(x)
if(na.rm) ratings <- na.omit(ratings)
ns <- nrow(ratings)
nr <- ncol(ratings)
#Without correction for ties
if (!correct) {
#Test for ties
TIES = FALSE
testties <- apply(ratings, 2, unique)
if (!is.matrix(testties)) TIES=TRUE
else { if (length(testties) < length(ratings)) TIES=TRUE }
ratings.rank <- apply(ratings,2,rank)
coeff.name <- "W"
coeff <- (12*var(apply(ratings.rank,1,sum))*(ns-1))/(nr^2*(ns^3-ns))
}
else { #With correction for ties
ratings <- as.matrix(na.omit(ratings))
ns <- nrow(ratings)
nr <- ncol(ratings)
ratings.rank <- apply(ratings,2,rank)
Tj <- 0
for (i in 1:nr) {
rater <- table(ratings.rank[,i])
ties <- rater[rater>1]
l <- as.numeric(ties)
Tj <- Tj + sum(l^3-l)
}
coeff.name <- "Wt"
coeff <- (12*var(apply(ratings.rank,1,sum))*(ns-1))/(nr^2*(ns^3-ns)-nr*Tj)
}
if(test){
#test statistics
Xvalue <- nr*(ns-1)*coeff
df1 <- ns-1
names(df1) <- "df"
p.value <- pchisq(Xvalue, df1, lower.tail = FALSE)
method <- paste("Kendall's coefficient of concordance", coeff.name)
alternative <- paste(coeff.name, "is greater 0")
names(ns) <- "subjects"
names(nr) <- "raters"
names(Xvalue) <- "Kendall chi-squared"
names(coeff) <- coeff.name
rval <- list(#subjects = ns, raters = nr,
estimate = coeff, parameter=c(df1, ns, nr),
statistic = Xvalue, p.value = p.value,
alternative = alternative, method = method, data.name = dname)
class(rval) <- "htest"
} else {
rval <- coeff
}
if (!correct && TIES) warning("Coefficient may be incorrect due to ties")
return(rval)
}
CCC <- function(x, y, ci = "z-transform", conf.level = 0.95, na.rm = FALSE){
dat <- data.frame(x, y)
if(na.rm) dat <- na.omit(dat)
# id <- complete.cases(dat)
# nmissing <- sum(!complete.cases(dat))
# dat <- dat[id,]
N. <- 1 - ((1 - conf.level) / 2)
zv <- qnorm(N., mean = 0, sd = 1)
lower <- "lwr.ci"
upper <- "upr.ci"
k <- length(dat$y)
yb <- mean(dat$y)
sy2 <- var(dat$y) * (k - 1) / k
sd1 <- sd(dat$y)
xb <- mean(dat$x)
sx2 <- var(dat$x) * (k - 1) / k
sd2 <- sd(dat$x)
r <- cor(dat$x, dat$y)
sl <- r * sd1 / sd2
sxy <- r * sqrt(sx2 * sy2)
p <- 2 * sxy / (sx2 + sy2 + (yb - xb)^2)
delta <- (dat$x - dat$y)
rmean <- apply(dat, MARGIN = 1, FUN = mean)
blalt <- data.frame(mean = rmean, delta)
# Scale shift:
v <- sd1 / sd2
# Location shift relative to the scale:
u <- (yb - xb) / ((sx2 * sy2)^0.25)
# Variable C.b is a bias correction factor that measures how far the best-fit line deviates from a line at 45 degrees (a measure of accuracy). No deviation from the 45 degree line occurs when C.b = 1. See Lin (1989 page 258).
# C.b <- (((v + 1) / (v + u^2)) / 2)^-1
# The following taken from the Stata code for function "concord" (changed 290408):
C.b <- p / r
# Variance, test, and CI for asymptotic normal approximation (per Lin (March 2000) Biometrics 56:325-5):
sep = sqrt(((1 - ((r)^2)) * (p)^2 * (1 - ((p)^2)) / (r)^2 + (2 * (p)^3 * (1 - p) * (u)^2 / r) - 0.5 * (p)^4 * (u)^4 / (r)^2 ) / (k - 2))
ll = p - zv * sep
ul = p + zv * sep
# Statistic, variance, test, and CI for inverse hyperbolic tangent transform to improve asymptotic normality:
t <- log((1 + p) / (1 - p)) / 2
set = sep / (1 - ((p)^2))
llt = t - zv * set
ult = t + zv * set
llt = (exp(2 * llt) - 1) / (exp(2 * llt) + 1)
ult = (exp(2 * ult) - 1) / (exp(2 * ult) + 1)
if(ci == "asymptotic"){
rho.c <- as.data.frame(cbind(p, ll, ul))
names(rho.c) <- c("est", lower, upper)
rval <- list(rho.c = rho.c, s.shift = v, l.shift = u, C.b = C.b, blalt = blalt ) # , nmissing = nmissing)
}
else if(ci == "z-transform"){
rho.c <- as.data.frame(cbind(p, llt, ult))
names(rho.c) <- c("est", lower, upper)
rval <- list(rho.c = rho.c, s.shift = v, l.shift = u, C.b = C.b, blalt = blalt) #, nmissing = nmissing)
}
return(rval)
}
KrippAlpha <- function (x, method = c("nominal", "ordinal", "interval", "ratio")) {
method <- match.arg(method)
coincidence.matrix <- function(x) {
levx <- (levels(as.factor(x)))
nval <- length(levx)
cm <- matrix(rep(0, nval * nval), nrow = nval)
dimx <- dim(x)
vn <- function(datavec) sum(!is.na(datavec))
if(any(is.na(x))) mc <- apply(x, 2, vn) - 1
else mc <- rep(1, dimx[2])
for(col in 1:dimx[2]) {
for(i1 in 1:(dimx[1] - 1)) {
for(i2 in (i1 + 1):dimx[1]) {
if(!is.na(x[i1, col]) && !is.na(x[i2, col])) {
index1 <- which(levx == x[i1, col])
index2 <- which(levx == x[i2, col])
cm[index1, index2] <- cm[index1,index2] + (1 + (index1 == index2))/mc[col]
if(index1 != index2) cm[index2,index1] <- cm[index1,index2]
}
}
}
}
nmv <- sum(apply(cm, 2, sum))
return(structure(list(method="Krippendorff's alpha",
subjects=dimx[2], raters=dimx[1],irr.name="alpha",
value=NA,stat.name="nil",statistic=NULL,
cm=cm,data.values=levx,nmatchval=nmv,data.level=NA),
class = "irrlist"))
}
ka <- coincidence.matrix(x)
ka$data.level <- method
dimcm <- dim(ka$cm)
utcm <- as.vector(ka$cm[upper.tri(ka$cm)])
diagcm <- diag(ka$cm)
occ <- sum(diagcm)
nc <- apply(ka$cm,1,sum)
ncnc <- sum(nc * (nc - 1))
dv <- as.numeric(ka$data.values)
diff2 <- rep(0,length(utcm))
ncnk <- rep(0,length(utcm))
ck <- 1
if (dimcm[2]<2)
ka$value <- 1.0
else {
for(k in 2:dimcm[2]) {
for(c in 1:(k-1)) {
ncnk[ck] <- nc[c] * nc[k]
if(match(method[1],"nominal",0)) diff2[ck] <- 1
if(match(method[1],"ordinal",0)) {
diff2[ck] <- nc[c]/2
if(k > (c+1))
for(g in (c+1):(k-1)) diff2[ck] <- diff2[ck] + nc[g]
diff2[ck] <- diff2[ck]+nc[k]/2
diff2[ck] <- diff2[ck]^2
}
if(match(method[1],"interval",0)) diff2[ck] <- (dv[c]-dv[k])^2
if(match(method[1],"ratio",0)) diff2[ck] <- (dv[c]-dv[k])^2/(dv[c]+dv[k])^2
ck <- ck+1
}
}
ka$value <- 1-(ka$nmatchval-1)*sum(utcm*diff2)/sum(ncnk*diff2)
}
return(ka)
}
Entropy <- function(x, y = NULL, base = 2, ...) {
# x is either a table or a vector if y is defined
if(!is.null(y)) { x <- table(x, y, ...) }
x <- as.matrix(x)
ptab <- x / sum(x)
H <- - sum( ifelse(ptab > 0, ptab * log(ptab, base=base), 0) )
return(H)
}
MutInf <- function(x, y = NULL, base = 2, ...){
# ### Ref.: http://en.wikipedia.org/wiki/Cluster_labeling
if(!is.null(y)) { x <- table(x, y, ...) }
x <- as.matrix(x)
return(
Entropy(rowSums(x), base=base) +
Entropy(colSums(x), base=base) - Entropy(x, base=base)
)
}
# Rao's Diversity from ade4 divc
# author:
DivCoef <- function(df, dis = NULL, scale = FALSE){
# checking of user's data and initialization.
if (!inherits(df, "data.frame")) stop("Non convenient df")
if (any(df < 0)) stop("Negative value in df")
if (!is.null(dis)) {
if (!inherits(dis, "dist")) stop("Object of class 'dist' expected for distance")
if (!IsEuclid(dis)) warning("Euclidean property is expected for distance")
dis <- as.matrix(dis)
if (nrow(df)!= nrow(dis)) stop("Non convenient df")
dis <- as.dist(dis)
}
if (is.null(dis)) dis <- as.dist((matrix(1, nrow(df), nrow(df))
- diag(rep(1, nrow(df)))) * sqrt(2))
div <- as.data.frame(rep(0, ncol(df)))
names(div) <- "diversity"
rownames(div) <- names(df)
for (i in 1:ncol(df)) {
if(sum(df[, i]) < 1e-16) div[i, ] <- 0
else div[i, ] <- (t(df[, i]) %*% (as.matrix(dis)^2) %*% df[, i]) / 2 / (sum(df[, i])^2)
}
if(scale == TRUE){
divmax <- DivCoefMax(dis)$value
div <- div / divmax
}
return(div)
}
IsEuclid <- function (distmat, plot = FALSE, print = FALSE, tol = 1e-07) {
"bicenter.wt" <- function (X, row.wt = rep(1, nrow(X)), col.wt = rep(1, ncol(X))) {
X <- as.matrix(X)
n <- nrow(X)
p <- ncol(X)
if (length(row.wt) != n)
stop("length of row.wt must equal the number of rows in x")
if (any(row.wt < 0) || (sr <- sum(row.wt)) == 0)
stop("weights must be non-negative and not all zero")
row.wt <- row.wt/sr
if (length(col.wt) != p)
stop("length of col.wt must equal the number of columns in x")
if (any(col.wt < 0) || (st <- sum(col.wt)) == 0)
stop("weights must be non-negative and not all zero")
col.wt <- col.wt/st
row.mean <- apply(row.wt * X, 2, sum)
col.mean <- apply(col.wt * t(X), 2, sum)
col.mean <- col.mean - sum(row.mean * col.wt)
X <- sweep(X, 2, row.mean)
X <- t(sweep(t(X), 2, col.mean))
return(X)
}
if (!inherits(distmat, "dist"))
stop("Object of class 'dist' expected")
if(any(distmat<tol))
warning("Zero distance(s)")
distmat <- as.matrix(distmat)
n <- ncol(distmat)
delta <- -0.5 * bicenter.wt(distmat * distmat)
lambda <- eigen(delta, symmetric = TRUE, only.values = TRUE)$values
w0 <- lambda[n]/lambda[1]
if (plot)
barplot(lambda)
if (print)
print(lambda)
return((w0 > -tol))
}
DivCoefMax <- function(dis, epsilon = 1e-008, comment = FALSE) {
# inititalisation
if(!inherits(dis, "dist")) stop("Distance matrix expected")
if(epsilon <= 0) stop("epsilon must be positive")
if(!IsEuclid(dis)) stop("Euclidean property is expected for dis")
D2 <- as.matrix(dis)^2 / 2
n <- dim(D2)[1]
result <- data.frame(matrix(0, n, 4))
names(result) <- c("sim", "pro", "met", "num")
relax <- 0 # determination de la valeur initiale x0
x0 <- apply(D2, 1, sum) / sum(D2)
result$sim <- x0 # ponderation simple
objective0 <- t(x0) %*% D2 %*% x0
if (comment == TRUE)
print("evolution of the objective function:")
xk <- x0 # grande boucle de test des conditions de Kuhn-Tucker
repeat {
# boucle de test de nullite du gradient projete
repeat {
maxi.temp <- t(xk) %*% D2 %*% xk
if(comment == TRUE) print(as.character(maxi.temp))
#calcul du gradient
deltaf <- (-2 * D2 %*% xk)
# determination des contraintes saturees
sature <- (abs(xk) < epsilon)
if(relax != 0) {
sature[relax] <- FALSE
relax <- 0
}
# construction du gradient projete
yk <- ( - deltaf)
yk[sature] <- 0
yk[!(sature)] <- yk[!(sature)] - mean(yk[!(
sature)])
# test de la nullite du gradient projete
if (max(abs(yk)) < epsilon) {
break
}
# determination du pas le plus grand compatible avec les contraintes
alpha.max <- as.vector(min( - xk[yk < 0] / yk[yk <
0]))
alpha.opt <- as.vector( - (t(xk) %*% D2 %*% yk) / (
t(yk) %*% D2 %*% yk))
if ((alpha.opt > alpha.max) | (alpha.opt < 0)) {
alpha <- alpha.max
}
else {
alpha <- alpha.opt
}
if (abs(maxi.temp - t(xk + alpha * yk) %*% D2 %*% (
xk + alpha * yk)) < epsilon) {
break
}
xk <- xk + alpha * yk
}
# verification des conditions de KT
if (prod(!sature) == 1) {
if (comment == TRUE)
print("KT")
break
}
vectD2 <- D2 %*% xk
u <- 2 * (mean(vectD2[!sature]) - vectD2[sature])
if (min(u) >= 0) {
if (comment == TRUE)
print("KT")
break
}
else {
if (comment == TRUE)
print("relaxation")
satu <- (1:n)[sature]
relax <- satu[u == min(u)]
relax <-relax[1]
}
}
if (comment == TRUE)
print(list(objective.init = objective0, objective.final
= maxi.temp))
result$num <- as.vector(xk, mode = "numeric")
result$num[result$num < epsilon] <- 0
# ponderation numerique
xk <- x0 / sqrt(sum(x0 * x0))
repeat {
yk <- D2 %*% xk
yk <- yk / sqrt(sum(yk * yk))
if (max(xk - yk) > epsilon) {
xk <- yk
}
else break
}
x0 <- as.vector(yk, mode = "numeric")
result$pro <- x0 / sum(x0) # ponderation propre
result$met <- x0 * x0 # ponderation propre
restot <- list()
restot$value <- DivCoef(cbind.data.frame(result$num), dis)[,1]
restot$vectors <- result
return(restot)
}
# http://sph.bu.edu/otlt/MPH-Modules/BS/BS704_Confidence_Intervals/BS704_Confidence_Intervals8.html
# sas: http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_surveyfreq_a0000000227.htm
# discussion: http://tolstoy.newcastle.edu.au/R/e2/help/06/11/4982.html
#
# RelRisk0 <- function(x, conf.level = NA) {
#
# rr <- (x[1,1]/sum(x[,1])) / (x[1,2]/sum(x[,2]))
# if (is.na(conf.level)) {
# res <- rr
# } else {
# sigma <- x[1,2]/(x[1,1]*sum(x[1,])) + x[2,2]/(x[2,1]*sum(x[2,]))
# qn <- qnorm(1-(1-conf.level)/2)
# ci <- exp(log(rr) + c(-1,1)*qn*sqrt(sigma))
# res <- c("rel. risk"=rr, lwr.ci=ci[1], upr.ci=ci[2])
# }
# return(res)
# }
RelRisk <- function(x, y = NULL, conf.level = NA, method = c("score", "wald", "use.or"), delta = 0.5, ...) {
if(!is.null(y)) x <- table(x, y, ...)
p <- (d <- dim(x))[1L]
if(!is.numeric(x) || length(d) != 2L || p != d[2L] || p !=2L)
stop("'x' is not a 2x2 numeric matrix")
x1 <- x[1,1]
x2 <- x[2,1]
n1 <- x[1,1] + x[1,2]
n2 <- x[2,1] + x[2,2]
rr <- (x[1,1]/sum(x[1,])) / (x[2,1]/sum(x[2,]))
if( !is.na(conf.level)) {
switch( match.arg( arg = method, choices = c("score", "wald", "use.or") )
, "score" = {
# source:
# Agresti-Code: http://www.stat.ufl.edu/~aa/cda/R/two-sample/R2/
# R Code for large-sample score confidence interval for a relative risk
# in a 2x2 table (Koopman 1984, Miettinen and Nurminen 1985, Nurminen 1986).
z = abs(qnorm((1-conf.level)/2))
if ((x2==0) &&(x1==0)){
ul = Inf
ll = 0
}
else{
a1 = n2*(n2*(n2+n1)*x1+n1*(n2+x1)*(z^2))
a2 = -n2*(n2*n1*(x2+x1)+2*(n2+n1)*x2*x1+n1*(n2+x2+2*x1)*(z^2))
a3 = 2*n2*n1*x2*(x2+x1)+(n2+n1)*(x2^2)*x1+n2*n1*(x2+x1)*(z^2)
a4 = -n1*(x2^2)*(x2+x1)
b1 = a2/a1
b2 = a3/a1
b3 = a4/a1
c1 = b2-(b1^2)/3
c2 = b3-b1*b2/3+2*(b1^3)/27
ceta = acos(sqrt(27)*c2/(2*c1*sqrt(-c1)))
t1 = -2*sqrt(-c1/3)*cos(pi/3-ceta/3)
t2 = -2*sqrt(-c1/3)*cos(pi/3+ceta/3)
t3 = 2*sqrt(-c1/3)*cos(ceta/3)
p01 = t1-b1/3
p02 = t2-b1/3
p03 = t3-b1/3
p0sum = p01+p02+p03
p0up = min(p01,p02,p03)
p0low = p0sum-p0up-max(p01,p02,p03)
if( (x2==0) && (x1!=0) ){
ll = (1-(n1-x1)*(1-p0low)/(x2+n1-(n2+n1)*p0low))/p0low
ul = Inf
}
else if( (x2!=n2) && (x1==0)){
ul = (1-(n1-x1)*(1-p0up)/(x2+n1-(n2+n1)*p0up))/p0up
ll = 0
}
else if( (x2==n2) && (x1==n1)){
ul = (n2+z^2)/n2
ll = n1/(n1+z^2)
}
else if( (x1==n1) || (x2==n2) ){
if((x2==n2) && (x1==0)) { ll = 0 }
if((x2==n2) && (x1!=0)) {
phat1 = x2/n2
phat2 = x1/n1
phihat = phat2/phat1
phil = 0.95*phihat
chi2 = 0
while (chi2 <= z){
a = (n2+n1)*phil
b = -((x2+n1)*phil+x1+n2)
c = x2+x1
p1hat = (-b-sqrt(b^2-4*a*c))/(2*a)
p2hat = p1hat*phil
q2hat = 1-p2hat
var = (n2*n1*p2hat)/(n1*(phil-p2hat)+n2*q2hat)
chi2 = ((x1-n1*p2hat)/q2hat)/sqrt(var)
ll = phil
phil = ll/1.0001}}
i = x2
j = x1
ni = n2
nj = n1
if( x1==n1 ){
i = x1
j = x2
ni = n1
nj = n2
}
phat1 = i/ni
phat2 = j/nj
phihat = phat2/phat1
phiu = 1.1*phihat
if((x2==n2) && (x1==0)) {
if(n2<100) {phiu = .01}
else {phiu=0.001}
}
chi1 = 0
while (chi1 >= -z){
a = (ni+nj)*phiu
b = -((i+nj)*phiu+j+ni)
c = i+j
p1hat = (-b-sqrt(b^2-4*a*c))/(2*a)
p2hat = p1hat*phiu
q2hat = 1-p2hat
var = (ni*nj*p2hat)/(nj*(phiu-p2hat)+ni*q2hat)
chi1 = ((j-nj*p2hat)/q2hat)/sqrt(var)
phiu1 = phiu
phiu = 1.0001*phiu1
}
if(x1==n1) {
ul = (1-(n1-x1)*(1-p0up)/(x2+n1-(n2+n1)*p0up))/p0up
ll = 1/phiu1
}
else{ ul = phiu1}
}
else{
ul = (1-(n1-x1)*(1-p0up)/(x2+n1-(n2+n1)*p0up))/p0up
ll = (1-(n1-x1)*(1-p0low)/(x2+n1-(n2+n1)*p0low))/p0low
}
}
}
, "wald" = {
# based on code by Michael Dewey, 2006
x1.d <- x1 + delta
x2.d <- x2 + delta
lrr <- log(rr)
se.lrr <- sqrt(1/x1.d - 1/n1 + 1/x2.d - 1/n2)
mult <- abs(qnorm((1-conf.level)/2))
ll <- exp(lrr - mult * se.lrr)
ul <- exp(lrr + mult * se.lrr)
}
, "use.or" = {
or <- OddsRatio(x, conf.level=conf.level)
p2 <- x2/n2
rr.ci <- or/((1-p2) + p2 * or)
ll <- unname(rr.ci[2])
ul <- unname(rr.ci[3])
}
)
}
if (is.na(conf.level)) {
res <- rr
} else {
res <- c("rel. risk"=rr, lwr.ci=ll, upr.ci=ul)
}
return(res)
}
OddsRatio <- function (x, conf.level = NULL, ...) {
UseMethod("OddsRatio")
}
OddsRatio.glm <- function(x, conf.level = NULL, digits=3, use.profile=FALSE, ...) {
if(is.null(conf.level)) conf.level <- 0.95
# Fasst die Ergebnisse eines binomialen GLMs als OR summary zusammen
d.res <- data.frame(summary(x)$coefficients)
names(d.res)[c(2,4)] <- c("Std. Error","Pr(>|z|)")
d.res$or <- exp(d.res$Estimate)
# ci or
d.res$"or.lci" <- exp(d.res$Estimate + qnorm(0.025)*d.res$"Std. Error" )
d.res$"or.uci" <- exp(d.res$Estimate + qnorm(0.975)*d.res$"Std. Error" )
if(use.profile)
ci <- exp(confint(x, level = conf.level))
else
ci <- exp(confint.default(x, level = conf.level))
# exclude na coefficients here, as summary does not yield those
d.res[, c("or.lci","or.uci")] <- ci[!is.na(coefficients(x)), ]
d.res$sig <- Format(d.res$"Pr(>|z|)", fmt="*")
d.res$pval <- Format(d.res$"Pr(>|z|)", fmt="p")
# d.res["(Intercept)",c("or","or.lci","or.uci")] <- NA
# d.res["(Intercept)","Pr(>|z|)"] <- "NA"
# d.res["(Intercept)"," "] <- ""
d.print <- data.frame(lapply(d.res[, 5:7], Format, digits=digits),
pval=d.res$pval, sig=d.res$sig, stringsAsFactors = FALSE)
rownames(d.print) <- rownames(d.res)
colnames(d.print)[4:5] <- c("Pr(>|z|)","")
mterms <- {
res <- lapply(labels(terms(x)), function(y)
colnames(model.matrix(formula(gettextf("~ 0 + %s", y)), data=model.frame(x))))
names(res) <- labels(terms(x))
res
}
res <- list(or=d.print, call=x$call,
BrierScore=BrierScore(x), PseudoR2=PseudoR2(x, which="all"), res=d.res,
nobs=nobs(x), terms=mterms, model=x$model)
class(res) <- "OddsRatio"
return(res)
}
OddsRatio.multinom <- function(x, conf.level=NULL, digits=3, ...) {
if(is.null(conf.level)) conf.level <- 0.95
# class(x) <- class(x)[class(x)!="regr"]
r.summary <- summary(x, Wald.ratios = TRUE)
coe <- t(r.summary$coefficients)
coe <- reshape(data.frame(coe, id=row.names(coe)), varying=1:ncol(coe), idvar="id"
, times=colnames(coe), v.names="or", direction="long")
se <- t(r.summary$standard.errors)
se <- reshape(data.frame(se), varying=1:ncol(se),
times=colnames(se), v.names="se", direction="long")[, "se"]
# d.res <- r.summary
d.res <- data.frame(
"or"= exp(coe[, "or"]),
"or.lci" = exp(coe[, "or"] + qnorm(0.025) * se),
"or.uci" = exp(coe[, "or"] - qnorm(0.025) * se),
"pval" = 2*(1-pnorm(q = abs(coe[, "or"]/se), mean=0, sd=1)),
"sig" = 2*(1-pnorm(q = abs(coe[, "or"]/se), mean=0, sd=1))
)
d.print <- data.frame(
"or"= Format(exp(coe[, "or"]), digits=digits),
"or.lci" = Format(exp(coe[, "or"] + qnorm(0.025) * se), digits=digits),
"or.uci" = Format(exp(coe[, "or"] - qnorm(0.025) * se), digits=digits),
"pval" = Format(2*(1-pnorm(q = abs(coe[, "or"]/se), mean=0, sd=1)), fmt="p", digits=3),
"sig" = Format(2*(1-pnorm(q = abs(coe[, "or"]/se), mean=0, sd=1)), fmt="*"),
stringsAsFactors = FALSE
)
colnames(d.print)[4:5] <- c("Pr(>|z|)","")
rownames(d.print) <- paste(coe$time, coe$id, sep=":")
rownames(d.res) <- rownames(d.print)
res <- list(or = d.print, call = x$call,
BrierScore = NA, # BrierScore(x),
PseudoR2 = PseudoR2(x, which="all"), res=d.res)
class(res) <- "OddsRatio"
return(res)
}
OddsRatio.zeroinfl <- function (x, conf.level = NULL, digits = 3, ...) {
if(is.null(conf.level)) conf.level <- 0.95
d.res <- data.frame(summary(x)$coefficients$zero)
names(d.res)[c(2, 4)] <- c("Std. Error", "Pr(>|z|)")
d.res$or <- exp(d.res$Estimate)
d.res$or.lci <- exp(d.res$Estimate + qnorm(0.025) * d.res$"Std. Error")
d.res$or.uci <- exp(d.res$Estimate + qnorm(0.975) * d.res$"Std. Error")
d.res["(Intercept)", c("or", "or.lci", "or.uci")] <- NA
d.res$sig <- format(as.character(cut(d.res$"Pr(>|z|)", breaks = c(0,
0.001, 0.01, 0.05, 0.1, 1), include.lowest = TRUE, labels = c("***",
"**", "*", ".", " "))), justify = "left")
d.res$"Pr(>|z|)" <- Format(d.res$"Pr(>|z|)", fmt="p")
d.res["(Intercept)", "Pr(>|z|)"] <- "NA"
d.res["(Intercept)", " "] <- ""
d.print <- data.frame(lapply(d.res[, 5:7], Format, digits=digits),
p.value = d.res[,4], sig = d.res[, 8], stringsAsFactors = FALSE)
rownames(d.print) <- rownames(d.res)
res <- list(or = d.print, call = x$call,
BrierScore = BrierScore(resp=(model.response(model.frame(x)) > 0) * 1L,
pred=predict(x, type="zero")),
PseudoR2 = PseudoR2(x, which="all"), res=d.res)
class(res) <- "OddsRatio"
return(res)
}
print.OddsRatio <- function(x, ...){
cat("\nCall:\n")
print(x$call)
cat("\nOdds Ratios:\n")
print(x$or)
cat("---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n\n")
if(!is.null(x$BrierScore)){
cat(gettextf("Brier Score: %s Nagelkerke R2: %s\n\n",
round(x$BrierScore,3), round(x$PseudoR2["Nagelkerke"],3)))
}
}
plot.OddsRatio <- function(x, intercept=FALSE, group=NULL, subset = NULL, ...){
if(!intercept)
# x$res <- x$res[rownames(x$res)!="(Intercept)", ]
x$res <- x$res[!grepl("(Intercept)", rownames(x$res)), ]
args <- list(...)
# here the defaults
args.errbars1 <- list(from=cbind(x$res$or, x$res$or.lci, x$res$or.uci))
# overwrite with userdefined values
if (!is.null(args[["args.errbars"]])) {
args.errbars1[names(args[["args.errbars"]])] <- args[["args.errbars"]][]
args[["args.errbars"]] <- NULL
}
# here the defaults for PlotDot
args.plotdot1 <- list(x=x$res$or, args.errbars=args.errbars1, labels=rownames(x$res),
panel.first=quote(abline(v=1, col="grey")))
if (!is.null(args)) {
args.plotdot1[names(args)] <- args
}
do.call(PlotDot, args=args.plotdot1)
}
OddsRatio.default <- function(x, conf.level = NULL, y = NULL, method=c("wald", "mle", "midp")
, interval = c(0, 1000), ...) {
if(!is.null(y)) x <- table(x, y, ...)
if(is.null(conf.level)) conf.level <- NA
p <- (d <- dim(x))[1L]
if(!is.numeric(x) || length(d) != 2L || p != d[2L] || p != 2L)
stop("'x' is not a 2x2 numeric matrix")
switch( match.arg( arg = method, choices = c("wald", "mle", "midp") )
, "wald" = {
if (any(x == 0)) x <- x + 0.5
lx <- log(x)
or <- exp(lx[1, 1] + lx[2, 2] - lx[1, 2] - lx[2, 1])
if(is.na(conf.level)){
res <- or
} else {
# Agresti Categorical Data Analysis, 3.1.1
sigma2lor <- sum(1/x)
ci <- or * exp(c(1,-1) * qnorm((1-conf.level)/2) * sqrt(sigma2lor))
res <- c("odds ratio"=or, lwr.ci=ci[1], upr.ci=ci[2])
}
}
, "mle" = {
if(is.na(conf.level)){
res <- unname(fisher.test(x, conf.int=FALSE)$estimate)
} else {
res <- fisher.test(x, conf.level=conf.level)
res <- c(res$estimate, lwr.ci=res$conf.int[1], upr.ci=res$conf.int[2])
}
}
, "midp" = {
# based on code from Tomas J. Aragon Developer <aragon at berkeley.edu>
a1 <- x[1,1]; a0 <- x[1,2]; b1 <- x[2,1]; b0 <- x[2,2]; or <- 1
# median-unbiased estimate function
mue <- function(a1, a0, b1, b0, or){
mm <- matrix(c(a1,a0,b1,b0), 2, 2, byrow=TRUE)
fisher.test(mm, or=or, alternative="l")$p-fisher.test(x=x, or=or, alternative="g")$p
}
##mid-p function
midp <- function(a1, a0, b1, b0, or = 1){
mm <- matrix(c(a1,a0,b1,b0),2,2, byrow=TRUE)
lteqtoa1 <- fisher.test(mm,or=or,alternative="l")$p.val
gteqtoa1 <- fisher.test(mm,or=or,alternative="g")$p.val
0.5*(lteqtoa1-gteqtoa1+1)
}
# root finding
EST <- uniroot(
function(or){ mue(a1, a0, b1, b0, or)},
interval = interval)$root
if(is.na(conf.level)){
res <- EST
} else {
alpha <- 1 - conf.level
LCL <- uniroot(function(or){
1-midp(a1, a0, b1, b0, or)-alpha/2
}, interval = interval)$root
UCL <- 1/uniroot(function(or){
midp(a1, a0, b1, b0, or=1/or)-alpha/2
}, interval = interval)$root
res <- c("odds ratio" = EST, lwr.ci=LCL, upr.ci= UCL)
}
}
)
return(res)
}
## odds ratio (OR) to relative risk (RR)
ORToRelRisk <- function(...) {
UseMethod("ORToRelRisk")
}
ORToRelRisk.default <- function(or, p0, ...) {
if(any(or <= 0))
stop("'or' has to be positive")
if(!all(ZeroIfNA(p0) %[]% c(0,1)))
stop("'p0' has to be in (0,1)")
or / (1 - p0 + p0*or)
}
ORToRelRisk.OddsRatio <- function(x, ...){
.PredPrevalence <- function(model) {
isNumericPredictor <- function(model, term){
unname(attr(attr(model, "terms"), "dataClasses")[term] == "numeric")
}
# mean of response ist used for all numeric predictors
meanresp <- mean(as.numeric(model.response(model)) - 1)
# this is ok, as the second level is the one we predict in glm
# https://stackoverflow.com/questions/23282048/logistic-regression-defining-reference-level-in-r
preds <- attr(terms(model), "term.labels")
# first the intercept
res <- NA_real_
for(i in seq_along(preds))
if(isNumericPredictor(model=model, term=preds[i]))
res <- c(res, meanresp)
else {
# get the proportions of the levels of the factor with the response ...
fprev <- prop.table(table(model.frame(model)[, preds[i]],
model.response(model)), 1)
# .. and use the proportion of positive response of the reference level
res <- c(res, rep(fprev[1, 2], times=nrow(fprev)-1))
}
return(res)
}
or <- x$res[, c("or", "or.lci", "or.uci")]
pprev <- .PredPrevalence(x$model)
res <- sapply(or, function(x) ORToRelRisk(x, pprev))
rownames(res) <- rownames(or)
colnames(res) <- c("rr", "rr.lci", "rr.uci")
return(res)
}
# Cohen, Jacob. 1988. Statistical power analysis for the behavioral
# sciences, (2nd edition). Lawrence Erlbaum Associates, Hillsdale, New
# Jersey, United States.
# Garson, G. David. 2007. Statnotes: Topics in Multivariate
# Analysis. URL:
# http://www2.chass.ncsu.edu/garson/pa765/statnote.htm. Visited Spring
# 2006 -- Summer 2007.
# Goodman, Leo A. and William H. Kruskal. 1954. Measures of Association
# for Cross-Classifications. Journal of the American Statistical
# Association, Vol. 49, No. 268 (December 1954), pp. 732-764.
# Liebetrau, Albert M. 1983. Measures of Association. Sage University
# Paper series on Quantitative Applications in the Social Sciences,
# 07-032. Sage Publications, Beverly Hills and London, United
# States/England.
# Margolin, Barry H. and Richard J. Light. 1974. An Analysis of Variance
# for Categorical Data II: Small Sample Comparisons with Chi Square and
# Other Competitors. Journal of the American Statistical Association,
# Vol. 69, No. 347 (September 1974), pp. 755-764.
# Reynolds, H. T. 1977. Analysis of Nominal Data. Sage University Paper
# series on Quantitative Applications in the Social Sciences, 08-007,
# Sage Publications, Beverly Hills/London, California/UK.
# SAS Institute. 2007. Measures of Association
# http://support.sas.com/onlinedoc/913/getDoc/en/statug.hlp/freq_sect20.htm
# Visited January 2007.
# Theil, Henri. 1970. On the Estimation of Relationships Involving
# Qualitative Variables. The American Journal of Sociology, Vol. 76,
# No. 1 (July 1970), pp. 103-154.
# N.B. One should use the values for the significance of the
# Goodman-Kruskal lambda and Theil's UC with reservation, as these
# have been modeled to mimic the behavior of the same statistics
# in SPSS.
GoodmanKruskalTau <- function(x, y = NULL, direction = c("row", "column"), conf.level = NA, ...){
if(!is.null(y)) x <- table(x, y, ...)
n <- sum(x)
n.err.unconditional <- n^2
sum.row <- rowSums(x)
sum.col <- colSums(x)
switch( match.arg( arg = direction, choices = c("row", "column") )
, "column" = { # Tau Column|Row
for(i in 1:nrow(x))
n.err.unconditional <- n.err.unconditional-n*sum(x[i,]^2/sum.row[i])
n.err.conditional <- n^2-sum(sum.col^2)
tau.CR <- 1-(n.err.unconditional/n.err.conditional)
v <- n.err.unconditional/(n^2)
d <- n.err.conditional/(n^2)
f <- d*(v+1)-2*v
var.tau.CR <- 0
for(i in 1:nrow(x))
for(j in 1:ncol(x))
var.tau.CR <- var.tau.CR + x[i,j]*(-2*v*(sum.col[j]/n)+d*((2*x[i,j]/sum.row[i])-sum((x[i,]/sum.row[i])^2))-f)^2/(n^2*d^4)
ASE.tau.CR <- sqrt(var.tau.CR)
est <- tau.CR
sigma2 <- ASE.tau.CR^2
}
, "row" = { # Tau Row|Column
for(j in 1:ncol(x))
n.err.unconditional <- n.err.unconditional-n*sum(x[,j]^2/sum.col[j])
n.err.conditional <- n^2-sum(sum.row^2)
tau.RC <- 1-(n.err.unconditional/n.err.conditional)
v <- n.err.unconditional/(n^2)
d <- n.err.conditional/(n^2)
f <- d*(v+1)-2*v
var.tau.RC <- 0
for(i in 1:nrow(x))
for(j in 1:ncol(x))
var.tau.RC <- var.tau.RC + x[i,j]*(-2*v*(sum.row[i]/n)+d*((2*x[i,j]/sum.col[j])-sum((x[,j]/sum.col[j])^2))-f)^2/(n^2*d^4)
ASE.tau.RC <- sqrt(var.tau.RC)
est <- tau.RC
sigma2 <- ASE.tau.RC^2
}
)
if(is.na(conf.level)){
res <- est
} else {
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + est
res <- c(tauA=est, lwr.ci=ci[1], upr.ci=ci[2])
}
return(res)
}
# good description
# http://salises.mona.uwi.edu/sa63c/Crosstabs%20Measures%20for%20Nominal%20Data.htm
Lambda <- function(x, y = NULL, direction = c("symmetric", "row", "column"), conf.level = NA, ...){
if(!is.null(y)) x <- table(x, y, ...)
# Guttman'a lambda (1941), resp. Goodman Kruskal's Lambda (1954)
n <- sum(x)
csum <- colSums(x)
rsum <- rowSums(x)
rmax <- apply(x, 1, max)
cmax <- apply(x, 2, max)
max.rsum <- max(rsum)
max.csum <- max(csum)
nr <- nrow(x)
nc <- ncol(x)
switch( match.arg( arg = direction, choices = c("symmetric", "row", "column") )
, "symmetric" = { res <- 0.5*(sum(rmax, cmax) - (max.csum + max.rsum)) / (n - 0.5*(max.csum + max.rsum)) }
, "column" = { res <- (sum(rmax) - max.csum) / (n - max.csum) }
, "row" = { res <- (sum(cmax) - max.rsum) / (n - max.rsum) }
)
if(is.na(conf.level)){
res <- res
} else {
L.col <- matrix(,nc)
L.row <- matrix(,nr)
switch( match.arg( arg = direction, choices = c("symmetric", "row", "column") )
, "symmetric" = {
# How to see:
# http://support.sas.com/documentation/cdl/en/statugfreq/63124/PDF/default/statugfreq.pdf
# pp. 1744
# Author: Nina
l <- which.max(csum)
k <- which.max(rsum)
li <- apply(x,1,which.max)
ki <- apply(x,2,which.max)
w <- 2*n-max.csum-max.rsum
v <- 2*n -sum(rmax,cmax)
xx <- sum(rmax[li==l], cmax[ki==k], rmax[k], cmax[l])
y <- 8*n-w-v-2*xx
t <- rep(NA, length(li))
for (i in 1:length(li)){
t[i] <- (ki[li[i]]==i & li[ki[li[i]]]==li[i])
}
sigma2 <- 1/w^4*(w*v*y-2 *w^2*(n - sum(rmax[t]))-2*v^2*(n-x[k,l]))
}
, "column" = {
L.col.max <- min(which(csum == max.csum))
for(i in 1:nr) {
if(length(which(x[i, intersect(which(x[i,] == max.csum), which(x[i,] == max.rsum))] == n))>0)
L.col[i] <- min(which(x[i, intersect(which(x[i,] == max.csum), which(x[i,] == max.rsum))] == n))
else
if(x[i, L.col.max] == max.csum)
L.col[i] <- L.col.max
else
L.col[i] <- min(which(x[i,] == rmax[i]))
}
sigma2 <- (n-sum(rmax))*(sum(rmax) + max.csum -
2*(sum(rmax[which(L.col == L.col.max)])))/(n-max.csum)^3
}
, "row" = {
L.row.max <- min(which(rsum == max.rsum))
for(i in 1:nc) {
if(length(which(x[intersect(which(x[,i] == max.rsum), which(x[,i] == max.csum)),i] == n))>0)
L.row[i] <- min(which(x[i,intersect(which(x[i,] == max.csum), which(x[i,] == max.rsum))] == n))
else
if(x[L.row.max,i] == max.rsum)
L.row[i] <- L.row.max
else
L.row[i] <- min(which(x[,i] == cmax[i]))
}
sigma2 <- (n-sum(cmax))*(sum(cmax) + max.rsum -
2*(sum(cmax[which(L.row == L.row.max)])))/(n-max.rsum)^3
}
)
pr2 <- 1 - (1 - conf.level)/2
ci <- pmin(1, pmax(0, qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + res))
res <- c(lambda = res, lwr.ci=ci[1], upr.ci=ci[2])
}
return(res)
}
UncertCoef <- function(x, y = NULL, direction = c("symmetric", "row", "column"),
conf.level = NA, p.zero.correction = 1/sum(x)^2, ... ) {
# Theil's UC (1970)
# slightly nudge zero values so that their logarithm can be calculated (cf. Theil 1970: x->0 => xlogx->0)
if(!is.null(y)) x <- table(x, y, ...)
x[x == 0] <- p.zero.correction
n <- sum(x)
rsum <- rowSums(x)
csum <- colSums(x)
hx <- -sum((apply(x, 1, sum) * log(apply(x, 1, sum)/n))/n)
hy <- -sum((apply(x, 2, sum) * log(apply(x, 2, sum)/n))/n)
hxy <- -sum(apply(x, c(1, 2), sum) * log(apply(x, c(1, 2), sum)/n)/n)
switch( match.arg( arg = direction, choices = c("symmetric", "row", "column") )
, "symmetric" = { res <- 2 * (hx + hy - hxy)/(hx + hy) }
, "row" = { res <- (hx + hy - hxy)/hx }
, "column" = { res <- (hx + hy - hxy)/hy }
)
if(!is.na(conf.level)){
var.uc.RC <- var.uc.CR <- 0
for(i in 1:nrow(x))
for(j in 1:ncol(x))
{ var.uc.RC <- var.uc.RC + x[i,j]*(hx*log(x[i,j]/csum[j])+((hy-hxy)*log(rsum[i]/n)))^2/(n^2*hx^4);
var.uc.CR <- var.uc.CR + x[i,j]*(hy*log(x[i,j]/rsum[i])+((hx-hxy)*log(csum[j]/n)))^2/(n^2*hy^4);
}
switch( match.arg( arg = direction, choices = c("symmetric", "row", "column") )
, "symmetric" = {
sigma2 <- 4*sum(x * (hxy * log(rsum %o% csum/n^2) - (hx+hy)*log(x/n))^2 ) /
(n^2*(hx+hy)^4)
}
, "row" = { sigma2 <- var.uc.RC }
, "column" = { sigma2 <- var.uc.CR }
)
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + res
res <- c(uc = res, lwr.ci=max(ci[1], -1), upr.ci=min(ci[2], 1))
}
return(res)
}
TheilU <- function(a, p, type = c(2, 1), na.rm = FALSE){
if(na.rm) {
idx <- complete.cases(a, p)
a <- a[idx]
p <- p[idx]
}
n <- length(a)
if(length(p)!=n) {
warning("a must have same length as p")
res <- NA
} else {
switch( match.arg(as.character(type), c("2", "1"))
, "1" = { res <- sqrt(sum((a-p)^2/n))/(sqrt(sum(a^2)/n) + sqrt(sum(p^2)/n)) }
, "2" = { res <- sqrt(sum((a-p)^2))/(sqrt(sum(a^2))) }
)
}
return(res)
}
#S function SomersDelta
#
# Calculates concordance probability and Somers' Dxy rank correlation
# between a variable X (for which ties are counted) and a binary
# variable Y (having values 0 and 1, for which ties are not counted).
# Uses short cut method based on average ranks in two groups.
#
# Usage:
#
# SomersDelta(x, y, weights)
#
# Returns vector whose elements are C Index, Dxy, n and missing, where
# C Index is the concordance probability and Dxy=2(C Index-.5).
#
# F. Harrell 28 Nov 90 6 Apr 98: added weights
#
# SomersDelta2 <- function(x, y, weights=NULL, normwt=FALSE, na.rm=TRUE) {
#
# wtd.mean <- function(x, weights=NULL, normwt='ignored', na.rm=TRUE)
# {
# if(!length(weights)) return(mean(x, na.rm=na.rm))
# if(na.rm) {
# s <- !is.na(x + weights)
# x <- x[s]
# weights <- weights[s]
# }
#
# sum(weights*x)/sum(weights)
# }
#
# wtd.table <- function(x, weights=NULL, type=c('list','table'),
# normwt=FALSE, na.rm=TRUE)
# {
# type <- match.arg(type)
# if(!length(weights))
# weights <- rep(1, length(x))
#
# isdate <- IsDate(x) ### 31aug02 + next 2
# ax <- attributes(x)
# ax$names <- NULL
# x <- if(is.character(x)) as.category(x)
# else unclass(x)
#
# lev <- levels(x)
# if(na.rm) {
# s <- !is.na(x + weights)
# x <- x[s,drop=FALSE] ### drop is for factor class
# weights <- weights[s]
# }
#
# n <- length(x)
# if(normwt)
# weights <- weights*length(x)/sum(weights)
#
# i <- order(x) ### R does not preserve levels here
# x <- x[i]; weights <- weights[i]
#
# if(any(diff(x)==0)) { ### slightly faster than any(duplicated(xo))
# weights <- tapply(weights, x, sum)
# if(length(lev)) { ### 3apr03
# levused <- lev[sort(unique(x))] ### 7sep02
# ### Next 3 lines 21apr03
# if((length(weights) > length(levused)) &&
# any(is.na(weights)))
# weights <- weights[!is.na(weights)]
#
# if(length(weights) != length(levused))
# stop('program logic error')
#
# names(weights) <- levused ### 10Apr01 length 16May01
# }
#
# if(!length(names(weights)))
# stop('program logic error') ### 16May01
#
# if(type=='table')
# return(weights)
#
# x <- all.is.numeric(names(weights),'vector')
# if(isdate)
# attributes(x) <- c(attributes(x),ax) ### 31aug02
#
# names(weights) <- NULL
# return(list(x=x, sum.of.weights=weights))
# }
#
# xx <- x ### 31aug02
# if(isdate)
# attributes(xx) <- c(attributes(xx),ax)
#
# if(type=='list')
# list(x=if(length(lev))lev[x]
# else xx,
# sum.of.weights=weights)
# else {
# names(weights) <- if(length(lev)) lev[x]
# else xx
# weights
# }
# }
#
#
# wtd.rank <- function(x, weights=NULL, normwt=FALSE, na.rm=TRUE)
# {
# if(!length(weights))
# return(rank(x),na.last=if(na.rm)NA else TRUE)
#
# tab <- wtd.table(x, weights, normwt=normwt, na.rm=na.rm)
#
# freqs <- tab$sum.of.weights
# ### rank of x = ### <= x - .5 (# = x, minus 1)
# r <- cumsum(freqs) - .5*(freqs-1)
# ### Now r gives ranks for all unique x values. Do table look-up
# ### to spread these ranks around for all x values. r is in order of x
# approx(tab$x, r, xout=x)$y
# }
#
#
# if(length(y)!=length(x))stop("y must have same length as x")
# y <- as.integer(y)
# wtpres <- length(weights)
# if(wtpres && (wtpres != length(x)))
# stop('weights must have same length as x')
#
# if(na.rm) {
# miss <- if(wtpres) is.na(x + y + weights)
# else is.na(x + y)
#
# nmiss <- sum(miss)
# if(nmiss>0) {
# miss <- !miss
# x <- x[miss]
# y <- y[miss]
# if(wtpres) weights <- weights[miss]
# }
# }
# else nmiss <- 0
#
# u <- sort(unique(y))
# if(any(! y %in% 0:1)) stop('y must be binary')
#
# if(wtpres) {
# if(normwt)
# weights <- length(x)*weights/sum(weights)
# n <- sum(weights)
# }
# else n <- length(x)
#
# if(n<2) stop("must have >=2 non-missing observations")
#
# n1 <- if(wtpres)sum(weights[y==1]) else sum(y==1)
#
# if(n1==0 || n1==n)
# return(c(C=NA,Dxy=NA,n=n,Missing=nmiss))
#
# mean.rank <- if(wtpres)
# wtd.mean(wtd.rank(x, weights, na.rm=FALSE), weights*y)
# else
# mean(rank(x)[y==1])
#
# c.index <- (mean.rank - (n1+1)/2)/(n-n1)
# dxy <- 2*(c.index-.5)
# r <- c(c.index, dxy, n, nmiss)
# names(r) <- c("C", "Dxy", "n", "Missing")
# r
# }
#
SomersDelta <- function(x, y = NULL, direction=c("row","column"), conf.level = NA, ...) {
if(!is.null(y)) tab <- table(x, y, ...)
else tab <- as.table(x)
# tab is a matrix of counts
x <- ConDisPairs(tab)
# use .DoCount
# if(is.na(conf.level)) {
# d.tab <- as.data.frame.table(tab)
# x <- .DoCount(d.tab[,1], d.tab[,2], d.tab[,3])
# } else {
# x <- ConDisPairs(tab)
# }
m <- min(dim(tab))
n <- sum(tab)
switch( match.arg( arg = direction, choices = c("row","column") )
, "row" = { ni. <- colSums(tab) }
, "column" = { ni. <- rowSums(tab) }
)
wt <- n^2 - sum(ni.^2)
# Asymptotic standard error: sqrt(sigma2)
sigma2 <- 4/wt^4 * (sum(tab * (wt*(x$pi.c - x$pi.d) - 2*(x$C-x$D)*(n-ni.))^2))
# debug: print(sqrt(sigma2))
somers <- (x$C - x$D) / (n * (n-1) /2 - sum(ni. * (ni. - 1) /2 ))
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + somers
if(is.na(conf.level)){
result <- somers
} else {
result <- c(somers = somers, lwr.ci=max(ci[1], -1), upr.ci=min(ci[2], 1))
}
return(result)
}
# Computes rank correlation measures between a variable X and a possibly
# censored variable Y, with event/censoring indicator EVENT
# Rank correlation is extension of Somers' Dxy = 2(Concordance Prob-.5)
# See Harrell et al JAMA 1984(?)
# Set outx=T to exclude ties in X from computations (-> Goodman-Kruskal
# gamma-type rank correlation)
# based on rcorr.cens in Hmisc, https://stat.ethz.ch/pipermail/r-help/2003-March/030837.html
# author Frank Harrell
# GoodmanGammaF <- function(x, y) {
# ### Fortran implementation of Concordant/Discordant, but still O(n^2)
# x <- as.numeric(x)
# y <- as.numeric(y)
# event <- rep(TRUE, length(x))
# if(length(y)!=length(x))
# stop("y must have same length as x")
# outx <- TRUE
# n <- length(x)
# ne <- sum(event)
# z <- .Fortran("cidxcn", x, y, event, length(x), nrel=double(1), nconc=double(1),
# nuncert=double(1),
# c.index=double(1), gamma=double(1), sd=double(1), as.logical(outx)
# )
# r <- c(z$c.index, z$gamma, z$sd, n, ne, z$nrel, z$nconc, z$nuncert)
# names(r) <- c("C Index","Dxy","S.D.","n","uncensored",
# "Relevant Pairs",
# "Concordant","Uncertain")
# unname(r[2])
# }
# GoodmanGamma(as.numeric(d.frm$Var1), as.numeric(d.frm$Var2))
# cor(as.numeric(d.frm$Var1), as.numeric(d.frm$Var2))
GoodmanKruskalGamma <- function(x, y = NULL, conf.level = NA, ...) {
if(!is.null(y)) tab <- table(x, y, ...)
else tab <- as.table(x)
# tab is a matrix of counts
# Based on code of Michael Friendly and Laura Thompson
# Confidence interval calculation and output from Greg Rodd
x <- ConDisPairs(tab)
psi <- 2 * (x$D * x$pi.c - x$C * x$pi.d)/(x$C + x$D)^2
# Asymptotic standard error: sqrt(sigma2)
sigma2 <- sum(tab * psi^2) - sum(tab * psi)^2
gamma <- (x$C - x$D)/(x$C + x$D)
if(is.na(conf.level)){
result <- gamma
} else {
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + gamma
result <- c(gamma = gamma, lwr.ci=max(ci[1], -1), upr.ci=min(ci[2], 1))
}
return(result)
}
# KendallTauB.table <- function(tab, conf.level = NA) {
# http://support.sas.com/documentation/cdl/en/statugfreq/63124/PDF/default/statugfreq.pdf
# pp 1738
# tab is a matrix of counts
# x <- ConDisPairs(tab)
# n <- sum(tab)
# ni. <- apply(tab, 1, sum)
# n.j <- apply(tab, 2, sum)
# wr <- n^2 - sum(ni.^2)
# wc <- n^2 - sum(n.j^2)
# w <- sqrt(wr * wc)
# vij <- ni. * wc + n.j * wr
# dij <- x$pi.c - x$pi.d ### Aij - Dij
# Asymptotic standard error: sqrt(sigma2)
# sigma2 <- 1/w^4 * (sum(tab * (2*w*dij + taub*vij)^2) - n^3 * taub^2 * (wr + wc)^2)
# this is the H0 = 0 variance:
# sigma2 <- 4/(wr * wc) * (sum(tab * (x$pi.c - x$pi.d)^2) - 4*(x$C - x$D)^2/n )
# taub <- 2*(x$C - x$D)/sqrt(wr * wc)
# if(is.na(conf.level)){
# result <- taub
# } else {
# pr2 <- 1 - (1 - conf.level)/2
# ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + taub
# result <- c(taub = taub, lwr.ci=max(ci[1], -1), ups.ci=min(ci[2], 1))
# }
# return(result)
# }
KendallTauA <- function(x, y = NULL, direction = c("row", "column"), conf.level = NA, ...){
if(!is.null(y)) tab <- table(x, y, ...)
else tab <- as.table(x)
x <- ConDisPairs(tab)
n <- sum(tab)
n0 <- n*(n-1)/2
taua <- (x$C - x$D) / n0
# Hollander, Wolfe pp. 415/416
# think we should not consider ties here, so take only the !=0 part
Ci <- as.vector((x$pi.c - x$pi.d) * (tab!=0))
Ci <- Ci[Ci!=0]
C_ <- sum(Ci)/n
sigma2 <- 2/(n*(n-1)) * ((2*(n-2))/(n*(n-1)^2) * sum((Ci - C_)^2) + 1 - taua^2)
if (is.na(conf.level)) {
result <- taua
}
else {
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + taua
result <- c(tau_a = taua, lwr.ci = max(ci[1], -1), upr.ci = min(ci[2], 1))
}
return(result)
}
# KendallTauB <- function(x, y = NULL, conf.level = NA, test=FALSE, alternative = c("two.sided", "less", "greater"), ...){
KendallTauB <- function(x, y = NULL, conf.level = NA, ...){
# Ref: http://www.fs.fed.us/psw/publications/lewis/LewisHMP.pdf
# pp 2-9
#
if (!is.null(y)) {
dname <- paste(deparse(substitute(x)), "and", deparse(substitute(y)))
} else {
dname <- deparse(substitute(x))
}
if(!is.null(y)) tab <- table(x, y, ...)
else tab <- as.table(x)
x <- ConDisPairs(tab)
n <- sum(tab)
n0 <- n*(n-1)/2
ti <- rowSums(tab) # apply(tab, 1, sum)
uj <- colSums(tab) # apply(tab, 2, sum)
n1 <- sum(ti * (ti-1) / 2)
n2 <- sum(uj * (uj-1) / 2)
taub <- (x$C - x$D) / sqrt((n0-n1)*(n0-n2))
pi <- tab / sum(tab)
pdiff <- (x$pi.c - x$pi.d) / sum(tab)
Pdiff <- 2 * (x$C - x$D) / sum(tab)^2
rowsum <- rowSums(pi) # apply(pi, 1, sum)
colsum <- colSums(pi) # apply(pi, 2, sum)
rowmat <- matrix(rep(rowsum, dim(tab)[2]), ncol = dim(tab)[2])
colmat <- matrix(rep(colsum, dim(tab)[1]), nrow = dim(tab)[1], byrow = TRUE)
delta1 <- sqrt(1 - sum(rowsum^2))
delta2 <- sqrt(1 - sum(colsum^2))
# Compute asymptotic standard errors taub
tauphi <- (2 * pdiff + Pdiff * colmat) * delta2 * delta1 + (Pdiff * rowmat * delta2)/delta1
sigma2 <- ((sum(pi * tauphi^2) - sum(pi * tauphi)^2)/(delta1 * delta2)^4) / n
# for very small pi/tauph it's possible that sigma2 gets negative so we cut small negative values here
# example: KendallTauB(table(iris$Species, iris$Species))
if(sigma2 < .Machine$double.eps * 10) sigma2 <- 0
if (is.na(conf.level)) {
result <- taub
}
else {
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + taub
result <- c(tau_b = taub, lwr.ci = max(ci[1], -1), upr.ci = min(ci[2], 1))
}
# if(test){
#
# alternative <- match.arg(alternative)
#
# zstat <- taub / sqrt(sigma2)
#
# if (alternative == "less") {
# pval <- pnorm(zstat)
# cint <- c(-Inf, zstat + qnorm(conf.level))
# }
# else if (alternative == "greater") {
# pval <- pnorm(zstat, lower.tail = FALSE)
# cint <- c(zstat - qnorm(conf.level), Inf)
# }
# else {
# pval <- 2 * pnorm(-abs(zstat))
# alpha <- 1 - conf.level
# cint <- qnorm(1 - alpha/2)
# cint <- zstat + c(-cint, cint)
# }
#
# RVAL <- list()
# RVAL$p.value <- pval
# RVAL$method <- "Kendall's rank correlation tau"
# RVAL$data.name <- dname
# RVAL$statistic <- x$C - x$D
# names(RVAL$statistic) <- "T"
# RVAL$estimate <- taub
# names(RVAL$estimate) <- "tau-b"
# RVAL$conf.int <- c(max(ci[1], -1), min(ci[2], 1))
# # attr(RVAL$conf.int, "conf.level") = round(attr(ci,"conf.level"), 3)
# class(RVAL) <- "htest"
# return(RVAL)
#
# # rval <- list(statistic = zstat, p.value = pval,
# # parameter = sd_pop,
# # conf.int = cint, estimate = estimate, null.value = mu,
# # alternative = alternative, method = method, data.name = dname)
#
# } else {
return(result)
# }
}
# KendallTauB(x, y, conf.level = 0.95, test=TRUE)
#
# cor.test(x,y, method="kendall")
# tab <- as.table(rbind(c(26,26,23,18,9),c(6,7,9,14,23)))
# KendallTauB(tab, conf.level = 0.95)
# Assocs(tab)
StuartTauC <- function(x, y = NULL, conf.level = NA, ...) {
if(!is.null(y)) tab <- table(x, y, ...)
else tab <- as.table(x)
# Reference:
# http://v8doc.sas.com/sashtml/stat/chap28/sect18.htm
x <- ConDisPairs(tab)
m <- min(dim(tab))
n <- sum(tab)
# Asymptotic standard error: sqrt(sigma2)
sigma2 <- 4 * m^2 / ((m-1)^2 * n^4) * (sum(tab * (x$pi.c - x$pi.d)^2) - 4 * (x$C -x$D)^2/n)
# debug: print(sqrt(sigma2))
# Tau-c = (C - D)*[2m/(n2(m-1))]
tauc <- (x$C - x$D) * 2 * min(dim(tab)) / (sum(tab)^2*(min(dim(tab))-1))
if(is.na(conf.level)){
result <- tauc
} else {
pr2 <- 1 - (1 - conf.level)/2
CI <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + tauc
result <- c(tauc = tauc, lwr.ci=max(CI[1], -1), upr.ci=min(CI[2], 1))
}
return(result)
}
# SpearmanRho <- function(x, y = NULL, use = c("everything", "all.obs", "complete.obs",
# "na.or.complete","pairwise.complete.obs"), conf.level = NA ) {
#
# if(is.null(y)) {
# x <- Untable(x)
# y <- x[,2]
# x <- x[,1]
# }
# # Reference:
# # https://stat.ethz.ch/pipermail/r-help/2006-October/114319.html
# # fisher z transformation for calc SpearmanRho ci :
# # Conover WJ, Practical Nonparametric Statistics (3rd edition). Wiley 1999.
#
# # http://support.sas.com/documentation/cdl/en/statugfreq/63124/PDF/default/statugfreq.pdf
# # pp 1738
#
#
# # n <- sum(tab)
# # ni. <- apply(tab, 1, sum)
# # n.j <- apply(tab, 2, sum)
# # F <- n^3 - sum(ni.^3)
# # G <- n^3 - sum(n.j^3)
# # w <- 1/12*sqrt(F * G)
#
# # ### Asymptotic standard error: sqrt(sigma2)
# # sigma2 <- 1
# # ### debug: print(sqrt(sigma2))
#
# # ### Tau-c = (C - D)*[2m/(n2(m-1))]
# # est <- 1
#
# # if(is.na(conf.level)){
# # result <- tauc
# # } else {
# # pr2 <- 1 - (1 - conf.level)/2
# # CI <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + est
# # result <- c(SpearmanRho = est, lwr.ci=max(CI[1], -1), ups.ci=min(CI[2], 1))
# # }
#
# # return(result)
#
#
# # Ref:
# # http://www-01.ibm.com/support/docview.wss?uid=swg21478368
#
# use <- match.arg(use, choices=c("everything", "all.obs", "complete.obs",
# "na.or.complete","pairwise.complete.obs"))
#
# rho <- cor(as.numeric(x), as.numeric(y), method="spearman", use = use)
#
# e_fx <- exp( 2 * ((.5 * log((1+rho) / (1-rho))) - c(1, -1) *
# (abs(qnorm((1 - conf.level)/2))) * (1 / sqrt(sum(complete.cases(x,y)) - 3)) ))
# ci <- (e_fx - 1) / (e_fx + 1)
#
# if (is.na(conf.level)) {
# result <- rho
# } else {
# pr2 <- 1 - (1 - conf.level) / 2
# result <- c(rho = rho, lwr.ci = max(ci[1], -1), upr.ci = min(ci[2], 1))
# }
# return(result)
#
# }
# replaced by DescTools v 0.99.36
# as Untable() is a nogo for tables with high frequencies...
SpearmanRho <- function(x, y = NULL, use = c("everything", "all.obs", "complete.obs",
"na.or.complete","pairwise.complete.obs"), conf.level = NA ) {
if(is.null(y)) {
# implemented following
# https://support.sas.com/documentation/onlinedoc/stat/151/freq.pdf
# S. 3103
# http://support.sas.com/documentation/cdl/en/statugfreq/63124/PDF/default/statugfreq.pdf
# pp 1738
# Old References:
# https://stat.ethz.ch/pipermail/r-help/2006-October/114319.html
# fisher z transformation for calc SpearmanRho ci :
# Conover WJ, Practical Nonparametric Statistics (3rd edition). Wiley 1999.
n <- sum(x)
ni. <- apply(x, 1, sum)
n.j <- apply(x, 2, sum)
ri <- rank(rownames(x))
ci <- rank(colnames(x))
ri <- 1:nrow(x)
ci <- 1:ncol(x)
R1i <- c(sapply(seq_along(ri),
function(i) ifelse(i==1, 0, cumsum(ni.)[i-1]) + ni.[i]/2))
C1i <- c(sapply(seq_along(ci),
function(i) ifelse(i==1, 0, cumsum(n.j)[i-1]) + n.j[i]/2))
Ri <- R1i - n/2
Ci <- C1i - n/2
v <- sum(x * outer(Ri, Ci))
F <- n^3 - sum(ni.^3)
G <- n^3 - sum(n.j^3)
w <- 1/12*sqrt(F * G)
rho <- v/w
} else {
# http://www-01.ibm.com/support/docview.wss?uid=swg21478368
use <- match.arg(use, choices=c("everything", "all.obs", "complete.obs",
"na.or.complete","pairwise.complete.obs"))
rho <- cor(as.numeric(x), as.numeric(y), method="spearman", use = use)
n <- complete.cases(x,y)
}
e_fx <- exp( 2 * ((.5 * log((1+rho) / (1-rho))) - c(1, -1) *
(abs(qnorm((1 - conf.level)/2))) * (1 / sqrt(sum(n) - 3)) ))
ci <- (e_fx - 1) / (e_fx + 1)
if (is.na(conf.level)) {
result <- rho
} else {
if(identical(rho, 1)){ # will blast the fisher z transformation
result <- c(rho=1, lwr.ci=1, upr.ci=1)
} else {
pr2 <- 1 - (1 - conf.level) / 2
result <- c(rho = rho, lwr.ci = max(ci[1], -1), upr.ci = min(ci[2], 1))
}
}
return(result)
}
# Definitions:
# http://v8doc.sas.com/sashtml/stat/chap28/sect18.htm
ConDisPairs <-function(x){
# tab is a matrix of counts
# Based on code of Michael Friendly and Laura Thompson
# slooooow because of 2 nested for clauses O(n^2)
# this is NOT faster when implemented with a mapply(...)
# Lookin for alternatives in C
# http://en.verysource.com/code/1169955_1/kendl2.cpp.html
# cor(..., "kendall") is for dimensions better
n <- nrow(x)
m <- ncol(x)
pi.c <- pi.d <- matrix(0, nrow = n, ncol = m)
row.x <- row(x)
col.x <- col(x)
for(i in 1:n){
for(j in 1:m){
pi.c[i, j] <- sum(x[row.x<i & col.x<j]) + sum(x[row.x>i & col.x>j])
pi.d[i, j] <- sum(x[row.x<i & col.x>j]) + sum(x[row.x>i & col.x<j])
}
}
C <- sum(pi.c * x)/2
D <- sum(pi.d * x)/2
return(list(pi.c = pi.c, pi.d = pi.d, C = C, D = D))
}
BinTree <- function(n) {
ranks <- rep(0L, n)
yet.to.do <- 1:n
depth <- floor(logb(n, 2))
start <- as.integer(2^depth)
lastrow.length <- 1 + n - start
indx <- seq(1L, by = 2L, length = lastrow.length)
ranks[yet.to.do[indx]] <- start + 0:(length(indx) - 1L)
yet.to.do <- yet.to.do[-indx]
while (start > 1) {
start <- as.integer(start/2)
indx <- seq(1L, by = 2L, length = start)
ranks[yet.to.do[indx]] <- start + 0:(start - 1L)
yet.to.do <- yet.to.do[-indx]
}
return(ranks)
}
PlotBinTree <- function(x, main="Binary tree", horiz=FALSE, cex=1.0, col=1, ...){
bimean <- function(x){
(x[rep(c(TRUE, FALSE), length.out=length(x))] +
x[rep(c(FALSE, TRUE), length.out=length(x))]) / 2
}
n <- length(x)
s <- floor(log(n, 2))
# if(sortx)
# x <- sort(x)
# else
# x <- x[BinTree(length(x))]
lst <- list()
lst[[s+1]] <- 1:2^s
for(i in s:1){
lst[[i]] <- bimean(lst[[i+1]])
}
d.frm <- merge(
x=data.frame(x=x, binpos=BinTree(length(x))),
y=data.frame(xpos = unlist(lst),
ypos = -rep(1:length(lst), unlist(lapply(lst, length))),
pos = 1:(2^(s+1)-1)
), by.x="binpos", by.y="pos")
if(horiz){
Canvas(xlim=c(1, s+1.5), ylim=c(0, 2^s+1), main=main,
asp=FALSE, mar=c(0,0,2,0)+1 )
ii <- 0
for(i in 1L:(length(lst)-1)){
for(j in seq_along(lst[[i]])){
ii <- ii + 1
if(ii < n)
segments(y0=lst[[i]][j], x0=i, y1=lst[[i+1]][2*(j-1)+1], x1=i+1, col=col)
ii <- ii + 1
if(ii < n)
segments(y0=lst[[i]][j], x0=i, y1=lst[[i+1]][2*(j-1)+2], x1=i+1, col=col)
}
}
# Rotate positions for the text
# rotxy <- Rotate(d.frm$xpos, d.frm$ypos, theta=pi/2)
# d.frm$xpos <- rotxy$x
# d.frm$ypos <- rotxy$y
m <- d.frm$xpos
d.frm$xpos <- -d.frm$ypos
d.frm$ypos <- m
} else {
Canvas(xlim=c(0,2^s+1), ylim=c(-s,1)-1.5, main=main,
asp=FALSE, mar=c(0,0,2,0)+1, ...)
ii <- 0
for(i in 1L:(length(lst)-1)){
for(j in seq_along(lst[[i]])){
ii <- ii + 1
if(ii < n)
segments(x0=lst[[i]][j], y0=-i, x1=lst[[i+1]][2*(j-1)+1], y1=-i-1, col=col)
ii <- ii + 1
if(ii < n)
segments(x0=lst[[i]][j], y0=-i, x1=lst[[i+1]][2*(j-1)+2], y1=-i-1, col=col)
}
}
}
BoxedText(x=d.frm$xpos, y=d.frm$ypos, labels=d.frm$x, cex=cex,
border=NA, xpad = 0.5, ypad = 0.5)
invisible(d.frm)
}
.DoCount <- function(y, x, wts) {
# O(n log n):
# http://www.listserv.uga.edu/cgi-bin/wa?A2=ind0506d&L=sas-l&P=30503
if(missing(wts)) wts <- rep_len(1L, length(x))
ord <- order(y)
ux <- sort(unique(x))
n2 <- length(ux)
idx <- BinTree(n2)[match(x[ord], ux)] - 1L
y <- cbind(y,1)
res <- .Call("conc", PACKAGE="DescTools", y[ord,], as.double(wts[ord]),
as.integer(idx), as.integer(n2))
return(list(pi.c = NA, pi.d = NA, C = res[2], D = res[1], T=res[3], N=res[4]))
}
.assocs_condis <- function(x, y = NULL, conf.level = NA, ...) {
# (very) fast function for calculating all concordant/discordant pairs based measures
# all table operations are cheap compared to the counting of cons/disc...
# no implementation for confidence levels so far.
if(!is.null(y))
x <- table(x, y)
# we need rowsums and colsums, so tabling is mandatory...
# use weights
x <- as.table(x)
min_dim <- min(dim(x))
n <- sum(x)
ni. <- rowSums(x)
nj. <- colSums(x)
n0 <- n*(n-1L)/2
n1 <- sum(ni. * (ni.-1L) / 2)
n2 <- sum(nj. * (nj.-1L) / 2)
x <- as.data.frame(x)
z <- .DoCount(x[,1], x[,2], x[,3])
gamma <- (z$C - z$D)/(z$C + z$D)
somers_r <- (z$C - z$D) / (n0 - n2)
somers_c <- (z$C - z$D) / (n0 - n1)
taua <- (z$C - z$D) / n0
taub <- (z$C - z$D) / sqrt((n0-n1)*(n0-n2))
tauc <- (z$C - z$D) * 2 * min_dim / (n^2*(min_dim-1L))
if(is.na(conf.level)){
result <- c(gamma=gamma, somers_r=somers_r, somers_c=somers_c,
taua=taua, taub=taub, tauc=tauc)
} else {
# psi <- 2 * (x$D * x$pi.c - x$C * x$pi.d)/(x$C + x$D)^2
# # Asymptotic standard error: sqrt(sigma2)
# gamma_sigma2 <- sum(tab * psi^2) - sum(tab * psi)^2
#
# pr2 <- 1 - (1 - conf.level)/2
# ci <- qnorm(pr2) * sqrt(gamma_sigma2) * c(-1, 1) + gamma
# result <- c(gamma = gamma, lwr.ci=max(ci[1], -1), ups.ci=min(ci[2], 1))
result <- NA
}
return(result)
}
TablePearson <- function(x, scores.type="table") {
# based on Lecoutre
# https://stat.ethz.ch/pipermail/r-help/2005-July/076371.html
# but the test might not be correctly implemented (negative values in sqrt)
# Statistic
sR <- scores(x, 1, scores.type)
sC <- scores(x, 2, scores.type)
n <- sum(x)
Rbar <- sum(apply(x, 1, sum) * sR) / n
Cbar <- sum(apply(x, 2, sum) * sC) / n
ssr <- sum(x * (sR-Rbar)^2)
ssc <- sum(t(x) * (sC-Cbar)^2)
tmpij <- outer(sR, sC, FUN=function(a,b) return((a-Rbar)*(b-Cbar)))
ssrc <- sum(x*tmpij)
v <- ssrc
w <- sqrt(ssr*ssc)
r <- v/w
return(r)
}
TableSpearman <- function(x, scores.type="table"){
# following:
# https://stat.ethz.ch/pipermail/r-help/2005-July/076371.html
# tablespearman=function(x)
# {
# # Details algorithme manuel SAS PROC FREQ page 540
# # Statistic
# n=sum(x)
# nr=nrow(x)
# nc=ncol(x)
# tmpd=cbind(expand.grid(1:nr,1:nc))
# ind=rep(1:(nr*nc),as.vector(x))
# tmp=tmpd[ind,]
# rhos=cor(apply(tmp,2,rank))[1,2]
# # ASE
# Ri=scores(x,1,"ranks")- n/2
# Ci=scores(x,2,"ranks")- n/2
# sr=apply(x,1,sum)
# sc=apply(x,2,sum)
# F=n^3 - sum(sr^3)
# G=n^3 - sum(sc^3)
# w=(1/12)*sqrt(F*G)
# vij=data
# for (i in 1:nrow(x))
# {
# qi=0
# if (i<nrow(x))
# {
# for (k in i:nrow(x)) qi=qi+sum(x[k,]*Ci)
# }
# }
# for (j in 1:ncol(x))
# {
# qj=0
# if (j<ncol(x))
# {
# for (k in j:ncol(x)) qj=qj+sum(x[,k]*Ri)
# }
# vij[i,j]=n*(Ri[i]*Ci[j] +
# 0.5*sum(x[i,]*Ci)+0.5*sum(data[,j]*Ri) +qi+qj)
# }
#
#
# v=sum(data*outer(Ri,Ci))
# wij=-n/(96*w)*outer(sr,sc,FUN=function(a,b) return(a^2*G+b^2*F))
# zij=w*vij-v*wij
# zbar=sum(data*zij)/n
# vard=(1/(n^2*w^4))*sum(x*(zij-zbar)^2)
# ASE=sqrt(vard)
# # Test
# vbar=sum(x*vij)/n
# p1=sum(x*(vij-vbar)^2)
# p2=n^2*w^2
# var0=p1/p2
# stat=rhos/sqrt(var0)
#
# # Output
# out=list(estimate=rhos,ASE=ASE,name="Spearman
# correlation",bornes=c(-1,1))
# class(out)="ordtest"
# return(out)
# }
#
# #tablespearman(data)
}
# all association measures combined
Assocs <- function(x, conf.level = 0.95, verbose=NULL){
if(is.null(verbose)) verbose <- "3"
if(verbose != "3") conf.level <- NA
res <- rbind(
# "Phi Coeff." = c(Phi(x), NA, NA)
"Contingency Coeff." = c(ContCoef(x),NA, NA)
)
if(is.na(conf.level)){
res <- rbind(res, "Cramer V" = c(CramerV(x), NA, NA))
res <- rbind(res, "Kendall Tau-b" = c(KendallTauB(x), NA, NA))
} else {
res <- rbind(res, "Cramer V" = CramerV(x, conf.level=conf.level))
res <- rbind(res, "Kendall Tau-b" = c(KendallTauB(x, conf.level=conf.level)))
}
if(verbose=="3") {
# # this is from boot::corr combined with ci logic from cor.test
# r <- boot::corr(d=CombPairs(1:nrow(x), 1:ncol(x)), as.vector(x))
# the boot::corr does not respect ordinal values in dimnames
# so do it ourselves
r <- TablePearson(x)
r.ci <- CorCI(rho = r, n = sum(x), conf.level = conf.level)
res <- rbind(res
, "Goodman Kruskal Gamma" = GoodmanKruskalGamma(x, conf.level=conf.level)
# , "Kendall Tau-b" = KendallTauB(x, conf.level=conf.level)
, "Stuart Tau-c" = StuartTauC(x, conf.level=conf.level)
, "Somers D C|R" = SomersDelta(x, direction="column", conf.level=conf.level)
, "Somers D R|C" = SomersDelta(x, direction="r", conf.level=conf.level)
# , "Pearson Correlation" =c(cor.p$estimate, lwr.ci=cor.p$conf.int[1], upr.ci=cor.p$conf.int[2])
, "Pearson Correlation" =c(r.ci[1], lwr.ci=r.ci[2], upr.ci=r.ci[3])
, "Spearman Correlation" = SpearmanRho(x, conf.level=conf.level)
, "Lambda C|R" = Lambda(x, direction="column", conf.level=conf.level)
, "Lambda R|C" = Lambda(x, direction="row", conf.level=conf.level)
, "Lambda sym" = Lambda(x, direction="sym", conf.level=conf.level)
, "Uncertainty Coeff. C|R" = UncertCoef(x, direction="column", conf.level=conf.level)
, "Uncertainty Coeff. R|C" = UncertCoef(x, direction="row", conf.level=conf.level)
, "Uncertainty Coeff. sym" = UncertCoef(x, direction="sym", conf.level=conf.level)
, "Mutual Information" = c(MutInf(x),NA,NA)
) }
if(verbose=="3")
dimnames(res)[[2]][1] <- "estimate"
else
dimnames(res)[[2]] <- c("estimate", "lwr.ci", "upr.ci")
class(res) <- c("Assocs", class(res))
return(res)
}
print.Assocs <- function(x, digits=4, ...){
out <- apply(round(x, digits), 2, Format, digits=digits)
if(nrow(x) == 3){
} else {
out[c(1,16), 2:3] <- " -"
}
dimnames(out) <- dimnames(x)
print(data.frame(out), quote=FALSE)
}
## This is an exact copy from Hmisc
## Changes since sent to statlib: improved printing N matrix in print.hoeffd
HoeffD <- function(x, y) {
phoeffd <- function(d, n) {
d <- as.matrix(d); n <- as.matrix(n)
b <- d + 1/36/n
z <- .5*(pi^4)*n*b
zz <- as.vector(z)
zz[is.na(zz)] <- 1e30 # so approx won't bark
tabvals <- c(5297,4918,4565,4236,3930,
3648,3387,3146,2924,2719,2530,2355,
2194,2045,1908,1781,1663,1554,1453,
1359,1273,1192,1117,1047,0982,0921,
0864,0812,0762,0716,0673,0633,0595,
0560,0527,0496,0467,0440,0414,0390,
0368,0347,0327,0308,0291,0274,0259,
0244,0230,0217,0205,0194,0183,0173,
0163,0154,0145,0137,0130,0123,0116,
0110,0104,0098,0093,0087,0083,0078,
0074,0070,0066,0063,0059,0056,0053,
0050,0047,0045,0042,0025,0014,0008,
0005,0003,0002,0001)/10000
P <- ifelse(z<1.1 | z>8.5, pmax(1e-8,pmin(1,exp(.3885037-1.164879*z))),
matrix(approx(c(seq(1.1, 5,by=.05),
seq(5.5,8.5,by=.5)),
tabvals, zz)$y,
ncol=ncol(d)))
dimnames(P) <- dimnames(d)
P
}
if(!missing(y))
x <- cbind(x, y)
x[is.na(x)] <- 1e30
storage.mode(x) <-
# if(.R.)
"double"
# else
# "single"
p <- as.integer(ncol(x))
if(p<1)
stop("must have >1 column")
n <- as.integer(nrow(x))
if(n<5)
stop("must have >4 observations")
h <-
# if(.R.)
.Fortran("hoeffd", x, n, p, hmatrix=double(p*p), aad=double(p*p),
maxad=double(p*p), npair=integer(p*p),
double(n), double(n), double(n), double(n), double(n),
PACKAGE="DescTools")
# else
# .Fortran("hoeffd", x, n, p, hmatrix=single(p*p), npair=integer(p*p),
# single(n), single(n), single(n), single(n), single(n),
# single(n), integer(n))
nam <- dimnames(x)[[2]]
npair <- matrix(h$npair, ncol=p)
aad <- maxad <- NULL
# if(.R.) {
aad <- matrix(h$aad, ncol=p)
maxad <- matrix(h$maxad, ncol=p)
dimnames(aad) <- dimnames(maxad) <- list(nam, nam)
# }
h <- matrix(h$hmatrix, ncol=p)
h[h>1e29] <- NA
dimnames(h) <- list(nam, nam)
dimnames(npair) <- list(nam, nam)
P <- phoeffd(h, npair)
diag(P) <- NA
structure(list(D=30*h, n=npair, P=P, aad=aad, maxad=maxad), class="HoeffD")
}
print.HoeffD <- function(x, ...)
{
cat("D\n")
print(round(x$D,2))
if(length(aad <- x$aad)) {
cat('\navg|F(x,y)-G(x)H(y)|\n')
print(round(aad,4))
}
if(length(mad <- x$maxad)) {
cat('\nmax|F(x,y)-G(x)H(y)|\n')
print(round(mad,4))
}
n <- x$n
if(all(n==n[1,1]))
cat("\nn=",n[1,1],"\n")
else {
cat("\nn\n")
print(x$n)
}
cat("\nP\n")
P <- x$P
P <- ifelse(P<.0001,0,P)
p <- format(round(P,4))
p[is.na(P)] <- ""
print(p, quote=FALSE)
invisible()
}
AndriSignorell/DescTools documentation built on April 13, 2024, 6:33 a.m.
R Package Documentation
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Defines functions fn fn BinomRatioCI_old BinomDiffCI BinomCIn BinomCI BootCI LOF Outlier HodgesLehmann HuberM .huberM .wgt.himedian .tauHuber TukeyBiweight Hmean Mode MESS_auc AUC FindCorr print.CorPolychor CorCI FisherZInv FisherZ CorPart Quantile Median.Freq Median.factor Median.default Median Mgsub VarX EX VarN SDN Var.Freq MADCI MAD .NormWeights
Documented in AUC BinomCI BinomCIn BinomDiffCI BootCI CorCI CorPart EX FindCorr FisherZ FisherZInv Hmean HodgesLehmann HuberM LOF MAD MADCI Median Median.default Median.factor Median.Freq Mgsub Mode Outlier print.CorPolychor Quantile SDN TukeyBiweight Var.Freq VarN VarX
# Project: DescTools
# Chapter: Statistical functions and confidence intervals
#
# Purpose: Tools for descriptive statistics, the missing link...
# Univariat, pairwise bivariate, groupwise und multivariate
#
# Author: Andri Signorell
# Version: 0.99.x
#
# some aliases
# internal function, no use to export it.. (?)
.NormWeights <- function(x, weights, na.rm=FALSE, zero.rm=FALSE, normwt=FALSE) {
# Idea Henrik Bengtsson
# we remove values with zero (and negative) weight.
# This would:
# 1) take care of the case when all weights are zero,
# 2) it will most likely speed up the sorting.
if (na.rm){
keep <- !is.na(x) & !is.na(weights)
if(zero.rm)
# remove values with zero weights
keep <- keep & (weights > 0)
x <- x[keep]
weights <- weights[keep]
}
if(any(is.na(x)) | (!is.null(weights) & any(is.na(weights))))
return(NA_real_)
n <- length(x)
if (length(weights) != n)
stop("length of 'weights' must equal the number of rows in 'x'")
# x and weights have length=0
if(length(x)==0)
return(list(x = x, weights = x, wsum = NaN))
if (any(weights< 0) || (s <- sum(weights)) == 0)
stop("weights must be non-negative and not all zero")
# we could normalize the weights to sum up to 1
if (normwt)
weights <- weights * n/s
return(list(x=x, weights=as.double(weights), wsum=s))
}
MAD <- function(x, weights = NULL, center = Median, constant = 1.4826, na.rm = FALSE,
low = FALSE, high = FALSE) {
if (is.function(center)) {
fct <- center
center <- "fct"
if(is.null(weights))
center <- gettextf("%s(x)", center)
else
center <- gettextf("%s(x, weights=weights)", center)
center <- eval(parse(text = center))
}
if(!is.null(weights)) {
z <- .NormWeights(x, weights, na.rm=na.rm, zero.rm=TRUE)
res <- constant * Median(abs(z$x - center), weights = z$weights)
} else {
# fall back to mad(), if there are no weights
res <- mad(x, center = center, constant = constant, na.rm = na.rm, low=low, high=high)
}
return(res)
}
MADCI <- function(x, y = NULL, two.samp.diff = TRUE, gld.est = "TM",
conf.level = 0.95, sides = c("two.sided","left","right"),
na.rm = FALSE, ...) {
if (na.rm) x <- na.omit(x)
sides <- match.arg(sides, choices = c("two.sided","left","right"),
several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
asv.mad <- function(x, method = "TM"){
lambda <- fit.fkml(x, method = method)$lambda
m <- median(x)
mad.x <- mad(x)
fFinv <- dgl(c(m - mad.x, m + mad.x, m), lambda1 = lambda)
FFinv <- pgl(c(m - mad.x, m + mad.x), lambda1 = lambda)
A <- fFinv[1] + fFinv[2]
C <- fFinv[1] - fFinv[2]
B <- C^2 + 4*C*fFinv[3]*(1 - FFinv[2] - FFinv[1])
(1/(4 * A^2))*(1 + B/fFinv[3]^2)
}
alpha <- 1 - conf.level
z <- qnorm(1 - alpha/2)
est <- mad.x <- mad(x)
n.x <- length(x)
asv.x <- asv.mad(x, method = gld.est)
if(is.null(y)){
ci <- mad.x + c(-z, z) * sqrt(asv.x / n.x)
} else{
y <- y[!is.na(y)]
mad.y <- mad(y)
n.y <- length(y)
asv.y <- asv.mad(y, method = gld.est)
if(two.samp.diff){
est <- mad.x - mad.y
ci <- est + c(-z, z)*sqrt(asv.x/n.x + asv.y/n.y)
} else{
est <- (mad.x/mad.y)^2
log.est <- log(est)
var.est <- 4 * est * ((1/mad.y^2)*asv.x/n.x + (est/mad.y^2)*asv.y/n.y)
Var.log.est <- (1 / est^2) * var.est
ci <- exp(log.est + c(-z, z) * sqrt(Var.log.est))
}
}
res <- c(est, ci)
names(res) <- c("mad","lwr.ci","upr.ci")
if(sides=="left")
res[3] <- Inf
else if(sides=="right")
res[2] <- -Inf
return( res )
}
# from stats
SD <- function (x, weights = NULL, na.rm = FALSE, ...)
sqrt(Var(if (is.vector(x) || is.factor(x)) x else as.double(x),
weights=weights, na.rm = na.rm, ...))
Var <- function (x, ...)
UseMethod("Var")
Var.default <- function (x, weights = NULL, na.rm = FALSE, method = c("unbiased", "ML"), ...) {
if(is.null(weights)) {
res <- var(x=x, na.rm=na.rm)
} else {
z <- .NormWeights(x, weights, na.rm=na.rm, zero.rm=TRUE)
if (match.arg(method) == "ML")
return(as.numeric(stats::cov.wt(cbind(z$x), z$weights, method = "ML")$cov))
xbar <- sum(z$weights * x) / z$wsum
res <- sum(z$weights * ((z$x - xbar)^2))/(z$wsum - 1)
}
return(res)
}
Var.Freq <- function(x, breaks, ...) {
n <- sum(x$freq)
mu <- sum(head(MoveAvg(breaks, order=2, align="left"), -1) * x$perc)
s2 <- (sum(head(MoveAvg(breaks, order=2, align="left"), -1)^2 * x$freq) - n*mu^2) / (n-1)
return(s2)
}
Cov <- cov
Cor <- cor
SDN <- function(x, na.rm = FALSE){
sd(x, na.rm=na.rm) * sqrt(((n <- sum(!is.na(x)))-1) /n)
}
VarN <- function(x, na.rm = FALSE){
var(x, na.rm=na.rm) * ((n <- sum(!is.na(x)))-1) /n
}
EX <- function(x, p) sum(x * p)
VarX <- function(x, p) sum((x - EX(x, p))^2 * p)
# multiple gsub
Mgsub <- function(pattern, replacement, x, ...) {
if (length(pattern)!=length(replacement)) {
stop("pattern and replacement do not have the same length.")
}
result <- x
for (i in 1:length(pattern)) {
result <- gsub(pattern[i], replacement[i], result, ...)
}
result
}
# Length(x)
# Table(x)
# Log(x)
# Abs(x)
#
# "abs", "sign", "sqrt", "ceiling", "floor", "trunc", "cummax", "cummin", "cumprod", "cumsum",
# "log", "log10", "log2", "log1p", "acos", "acosh", "asin", "asinh", "atan", "atanh",
# "exp", "expm1", "cos", "cosh", "cospi", "sin", "sinh", "sinpi", "tan", "tanh",
# "tanpi", "gamma", "lgamma", "digamma", "trigamma"
Median <- function(x, ...)
UseMethod("Median")
Median.default <- function(x, weights = NULL, na.rm = FALSE, ...) {
if(is.null(weights))
median(x=x, na.rm=na.rm)
else
Quantile(x, weights, probs=0.5, na.rm=na.rm, names=FALSE)
}
# ordered interface for the median
Median.factor <- function(x, na.rm = FALSE, ...) {
# Answered by Hong Ooi on 2011-10-28T00:37:08-04:00
# http://www.rqna.net/qna/nuiukm-idiomatic-method-of-finding-the-median-of-an-ordinal-in-r.html
# return NA, if x is not ordered
# clearme: why not median.ordered?
if(!is.ordered(x)) return(NA)
if(na.rm) x <- na.omit(x)
if(any(is.na(x))) return(NA)
levs <- levels(x)
m <- median(as.integer(x), na.rm = na.rm)
if(floor(m) != m)
{
warning("Median is between two values; using the first one")
m <- floor(m)
}
ordered(m, labels = levs, levels = seq_along(levs))
}
Median.Freq <- function(x, breaks, ...) {
mi <- min(which(x$cumperc > 0.5))
breaks[mi] + (tail(x$cumfreq, 1)/2 - x[mi-1, "cumfreq"]) /
x[mi, "freq"] * diff(breaks[c(mi, mi+1)])
}
# further weighted quantiles in Hmisc and modi, both on CRAN
Quantile <- function(x, weights = NULL, probs = seq(0, 1, 0.25),
na.rm = FALSE, names=TRUE, type = 7, digits=7) {
if(is.null(weights)){
quantile(x=x, probs=probs, na.rm=na.rm, names=names, type=type, digits=digits)
} else {
# this is a not exported stats function
format_perc <- function (x, digits = max(2L, getOption("digits")), probability = TRUE,
use.fC = length(x) < 100, ...) {
if (length(x)) {
if (probability)
x <- 100 * x
ans <- paste0(if (use.fC)
formatC(x, format = "fg", width = 1, digits = digits)
else format(x, trim = TRUE, digits = digits, ...), "%")
ans[is.na(x)] <- ""
ans
}
else character(0)
}
sorted <- FALSE
# initializations
if (!is.numeric(x)) stop("'x' must be a numeric vector")
n <- length(x)
if (n == 0 || (!isTRUE(na.rm) && any(is.na(x)))) {
# zero length or missing values
return(rep.int(NA, length(probs)))
}
if (!is.null(weights)) {
if (!is.numeric(weights)) stop("'weights' must be a numeric vector")
else if (length(weights) != n) {
stop("'weights' must have the same length as 'x'")
} else if (!all(is.finite(weights))) stop("missing or infinite weights")
if (any(weights < 0)) warning("negative weights")
if (!is.numeric(probs) || all(is.na(probs)) ||
isTRUE(any(probs < 0 | probs > 1))) {
stop("'probs' must be a numeric vector with values in [0,1]")
}
if (all(weights == 0)) { # all zero weights
warning("all weights equal to zero")
return(rep.int(0, length(probs)))
}
}
# remove NAs (if requested)
if(isTRUE(na.rm)){
indices <- !is.na(x)
x <- x[indices]
n <- length(x)
if(!is.null(weights)) weights <- weights[indices]
}
# sort values and weights (if requested)
if(!isTRUE(sorted)) {
# order <- order(x, na.last=NA) ## too slow
order <- order(x)
x <- x[order]
weights <- weights[order] # also works if 'weights' is NULL
}
# some preparations
if(is.null(weights)) rw <- (1:n)/n
else rw <- cumsum(weights)/sum(weights)
# obtain quantiles
# currently only type 5
if (type == 5) {
qs <- sapply(probs,
function(p) {
if (p == 0) return(x[1])
else if (p == 1) return(x[n])
select <- min(which(rw >= p))
if(rw[select] == p) mean(x[select:(select+1)])
else x[select]
})
} else if(type == 7){
if(is.null(weights)){
index <- 1 + max(n - 1, 0) * probs
lo <- pmax(floor(index), 1)
hi <- ceiling(index)
x <- sort(x, partial = if (n == 0)
numeric()
else unique(c(lo, hi)))
qs <- x[lo]
i <- which((index > lo & x[hi] != qs))
h <- (index - lo)[i]
qs[i] <- (1 - h) * qs[i] + h * x[hi[i]]
} else {
n <- sum(weights)
ord <- 1 + (n - 1) * probs
low <- pmax(floor(ord), 1)
high <- pmin(low + 1, n)
ord <- ord %% 1
## Find low and high order statistics
## These are minimum values of x such that the cum. freqs >= c(low,high)
allq <- approx(cumsum(weights), x, xout=c(low, high),
method='constant', f=1, rule=2)$y
k <- length(probs)
qs <- (1 - ord)*allq[1:k] + ord*allq[-(1:k)]
}
} else {
qs <- NA
warning(gettextf("type %s is not implemented", type))
}
# return(unname(q))
# why unname? change to named.. 14.10.2020
if (names && length(probs) > 0L) {
stopifnot(is.numeric(digits), digits >= 1)
names(qs) <- format_perc(probs, digits = digits)
}
return(qs)
}
}
IQRw <- function (x, weights = NULL, na.rm = FALSE, type = 7) {
if(is.null(weights))
IQR(x=x, na.rm=na.rm, type=type)
else
diff(Quantile(x, weights=weights, probs=c(0.25, 0.75), na.rm=na.rm, type=type))
}
## stats: functions (RobRange, Hmean, Gmean, Aad, HuberM etc.) ====
CorPart <- function(m, x, y) {
cl <- match.call()
if(dim(m)[1] != dim(m)[2]) {
n.obs <- dim(m)[1]
m <- cor(m, use="pairwise")
}
if(!is.matrix(m)) m <- as.matrix(m)
# first reorder the matrix to select the right variables
nm <- dim(m)[1]
t.mat <- matrix(0, ncol=nm, nrow=nm)
xy <- c(x,y)
numx <- length(x)
numy <- length(y)
nxy <- numx+numy
for (i in 1:nxy) {
t.mat[i, xy[i]] <- 1
}
reorder <- t.mat %*% m %*% t(t.mat)
reorder[abs(reorder) > 1] <- NA # this allows us to use the matrix operations to reorder and pick
X <- reorder[1:numx, 1:numx]
Y <- reorder[1:numx, (numx+1):nxy]
phi <- reorder[(numx+1):nxy,(numx+1):nxy]
phi.inv <- solve(phi)
X.resid <- X - Y %*% phi.inv %*% t(Y)
sd <- diag(sqrt(1/diag(X.resid)))
X.resid <- sd %*% X.resid %*% sd
colnames(X.resid) <- rownames(X.resid) <- colnames(m)[x]
return(X.resid)
}
FisherZ <- function(rho) {0.5*log((1+rho)/(1-rho)) } #converts r to z
FisherZInv <- function(z) {(exp(2*z)-1)/(1+exp(2*z)) } #converts back again
CorCI <- function(rho, n, conf.level = 0.95, alternative = c("two.sided","less","greater")) {
if (n < 3L)
stop("not enough finite observations")
if (!missing(conf.level) && (length(conf.level) != 1 || !is.finite(conf.level)
|| conf.level < 0 || conf.level > 1))
stop("'conf.level' must be a single number between 0 and 1")
alternative <- match.arg(alternative)
# correct rho == 1 with rho == almost 1 in order to return ci = c(1, 1)
# which is a sensible value for the confidence interval
if(identical(rho, 1))
ci <- c(1, 1)
else {
z <- FisherZ(rho)
sigma <- 1/sqrt(n - 3)
ci <- switch(alternative,
less = c(-Inf, z + sigma * qnorm(conf.level)),
greater = c(z - sigma * qnorm(conf.level), Inf),
two.sided = z + c(-1, 1) * sigma * qnorm((1 + conf.level)/2))
ci <- FisherZInv(ci)
}
return(c(cor = rho, lwr.ci = ci[1], upr.ci = ci[2]))
}
CorPolychor <- function (x, y, ML=FALSE, control=list(), std.err=FALSE, maxcor=.9999){
# last modified 21 Oct 08 by J. Fox
binBvn <- function(rho, row.cuts, col.cuts, bins=4){
# last modified 29 Mar 07 by J. Fox
row.cuts <- if (missing(row.cuts)) c(-Inf, 1:(bins - 1)/bins, Inf) else c(-Inf, row.cuts, Inf)
col.cuts <- if (missing(col.cuts)) c(-Inf, 1:(bins - 1)/bins, Inf) else c(-Inf, col.cuts, Inf)
r <- length(row.cuts) - 1
c <- length(col.cuts) - 1
P <- matrix(0, r, c)
R <- matrix(c(1, rho, rho, 1), 2, 2)
for (i in 1:r){
for (j in 1:c){
P[i,j] <- pmvnorm(lower=c(row.cuts[i], col.cuts[j]),
upper=c(row.cuts[i+1], col.cuts[j+1]),
corr=R)
}
}
P
}
f <- function(pars) {
if (length(pars) == 1){
rho <- pars
if (abs(rho) > maxcor) rho <- sign(rho)*maxcor
row.cuts <- rc
col.cuts <- cc
}
else {
rho <- pars[1]
if (abs(rho) > maxcor) rho <- sign(rho)*maxcor
row.cuts <- pars[2:r]
col.cuts <- pars[(r+1):(r+c-1)]
}
P <- binBvn(rho, row.cuts, col.cuts)
- sum(tab * log(P))
}
tab <- if (missing(y)) x else table(x, y)
zerorows <- apply(tab, 1, function(x) all(x == 0))
zerocols <- apply(tab, 2, function(x) all(x == 0))
zr <- sum(zerorows)
if (0 < zr) warning(paste(zr, " row", suffix <- if(zr == 1) "" else "s",
" with zero marginal", suffix," removed", sep=""))
zc <- sum(zerocols)
if (0 < zc) warning(paste(zc, " column", suffix <- if(zc == 1) "" else "s",
" with zero marginal", suffix, " removed", sep=""))
tab <- tab[!zerorows, ,drop=FALSE]
tab <- tab[, !zerocols, drop=FALSE]
r <- nrow(tab)
c <- ncol(tab)
if (r < 2) {
warning("the table has fewer than 2 rows")
return(NA)
}
if (c < 2) {
warning("the table has fewer than 2 columns")
return(NA)
}
n <- sum(tab)
rc <- qnorm(cumsum(rowSums(tab))/n)[-r]
cc <- qnorm(cumsum(colSums(tab))/n)[-c]
if (ML) {
result <- optim(c(optimise(f, interval=c(-1, 1))$minimum, rc, cc), f,
control=control, hessian=std.err)
if (result$par[1] > 1){
result$par[1] <- 1
warning("inadmissible correlation set to 1")
}
else if (result$par[1] < -1){
result$par[1] <- -1
warning("inadmissible correlation set to -1")
}
if (std.err) {
chisq <- 2*(result$value + sum(tab * log((tab + 1e-6)/n)))
df <- length(tab) - r - c
result <- list(type="polychoric",
rho=result$par[1],
row.cuts=result$par[2:r],
col.cuts=result$par[(r+1):(r+c-1)],
var=solve(result$hessian),
n=n,
chisq=chisq,
df=df,
ML=TRUE)
class(result) <- "polycor"
return(result)
}
else return(as.vector(result$par[1]))
}
else if (std.err){
result <- optim(0, f, control=control, hessian=TRUE, method="BFGS")
if (result$par > 1){
result$par <- 1
warning("inadmissible correlation set to 1")
}
else if (result$par < -1){
result$par <- -1
warning("inadmissible correlation set to -1")
}
chisq <- 2*(result$value + sum(tab *log((tab + 1e-6)/n)))
df <- length(tab) - r - c
result <- list(type="polychoric",
rho=result$par,
var=1/result$hessian,
n=n,
chisq=chisq,
df=df,
ML=FALSE)
class(result) <- "CorPolychor"
return(result)
}
else optimise(f, interval=c(-1, 1))$minimum
}
print.CorPolychor <- function(x, digits = max(3, getOption("digits") - 3), ...){
# last modified 24 June 04 by J. Fox
if (x$type == "polychoric"){
se <- sqrt(diag(x$var))
se.rho <- se[1]
est <- if (x$ML) "ML est." else "2-step est."
cat("\nPolychoric Correlation, ", est, " = ", signif(x$rho, digits),
" (", signif(se.rho, digits), ")", sep="")
if (x$df > 0)
cat("\nTest of bivariate normality: Chisquare = ",
signif(x$chisq, digits), ", df = ", x$df, ", p = ",
signif(pchisq(x$chisq, x$df, lower.tail=FALSE), digits), "\n", sep="")
else cat("\n")
r <- length(x$row.cuts)
c <- length(x$col.cuts)
if (r == 0) return(invisible(x))
row.cuts.se <- se[2:(r+1)]
col.cuts.se <- se[(r+2):(r+c+1)]
rowThresh <- signif(cbind(x$row.cuts, row.cuts.se), digits)
if (r > 1) cat("\n Row Thresholds\n")
else cat("\n Row Threshold\n")
colnames(rowThresh) <- c("Threshold", "Std.Err.")
rownames(rowThresh) <- if (r > 1) 1:r else " "
print(rowThresh)
colThresh <- signif(cbind(x$col.cuts, col.cuts.se), digits)
if (c > 1) cat("\n\n Column Thresholds\n")
else cat("\n\n Column Threshold\n")
colnames(colThresh) <- c("Threshold", "Std.Err.")
rownames(colThresh) <- if (c > 1) 1:c else " "
print(colThresh)
}
else if (x$type == "polyserial"){
se <- sqrt(diag(x$var))
se.rho <- se[1]
est <- if (x$ML) "ML est." else "2-step est."
cat("\nPolyserial Correlation, ", est, " = ", signif(x$rho, digits),
" (", signif(se.rho, digits), ")", sep="")
cat("\nTest of bivariate normality: Chisquare = ", signif(x$chisq, digits),
", df = ", x$df, ", p = ", signif(pchisq(x$chisq, x$df, lower.tail=FALSE), digits),
"\n\n", sep="")
if (length(se) == 1) return(invisible(x))
cuts.se <- se[-1]
thresh <- signif(rbind(x$cuts, cuts.se), digits)
colnames(thresh) <- 1:length(x$cuts)
rownames(thresh) <- c("Threshold", "Std.Err.")
print(thresh)
}
else print(unclass(x))
invisible(x)
}
FindCorr <- function(x, cutoff = .90, verbose = FALSE) {
# Author: Max Kuhn
# source library(caret)
varnum <- dim(x)[1]
if(!isTRUE(all.equal(x, t(x)))) stop("correlation matrix is not symmetric")
if(varnum ==1) stop("only one variable given")
x <- abs(x)
# re-ordered columns based on max absolute correlation
originalOrder <- 1:varnum
averageCorr <- function(x) mean(x, na.rm = TRUE)
tmp <- x
diag(tmp) <- NA
maxAbsCorOrder <- order(apply(tmp, 2, averageCorr), decreasing = TRUE)
x <- x[maxAbsCorOrder, maxAbsCorOrder]
newOrder <- originalOrder[maxAbsCorOrder]
deletecol <- 0
for(i in 1L:(varnum-1))
{
for(j in (i+1):varnum)
{
if(!any(i == deletecol) & !any(j == deletecol))
{
if(verbose)
cat("Considering row\t", newOrder[i],
"column\t", newOrder[j],
"value\t", round(x[i,j], 3), "\n")
if(abs(x[i,j]) > cutoff)
{
if(mean(x[i, -i]) > mean(x[-j, j]))
{
deletecol <- unique(c(deletecol, i))
if(verbose) cat(" Flagging column\t", newOrder[i], "\n")
} else {
deletecol <- unique(c(deletecol, j))
if(verbose) cat(" Flagging column\t", newOrder[j], "\n")
}
}
}
}
}
deletecol <- deletecol[deletecol != 0]
newOrder[deletecol]
}
# Alternative:
# From roc bioconductor
# Vince Carey (stvjc@channing.harvard.edu)
# trapezint <- function (x, y, a, b){
#
# if (length(x) != length(y))
# stop("length x must equal length y")
# y <- y[x >= a & x <= b]
# x <- x[x >= a & x <= b]
# if (length(unique(x)) < 2)
# return(NA)
# ya <- approx(x, y, a, ties = max, rule = 2)$y
# yb <- approx(x, y, b, ties = max, rule = 2)$y
# x <- c(a, x, b)
# y <- c(ya, y, yb)
# h <- diff(x)
# lx <- length(x)
# 0.5 * sum(h * (y[-1] + y[-lx]))
# }
# AUC_deprecated <- function(x, y, from=min(x, na.rm=TRUE), to = max(x, na.rm=TRUE),
# method=c("trapezoid", "step", "spline", "linear"),
# absolutearea = FALSE, subdivisions = 100, na.rm = FALSE, ...) {
#
# # calculates Area unter the curve
# # example:
# # AUC( x=c(1,2,3,5), y=c(0,1,1,2))
# # AUC( x=c(2,3,4,5), y=c(0,1,1,2))
#
# if(na.rm) {
# idx <- complete.cases(cbind(x,y))
# x <- x[idx]
# y <- y[idx]
# }
#
# if (length(x) != length(y))
# stop("length x must equal length y")
#
# idx <- order(x)
# x <- x[idx]
# y <- y[idx]
#
# switch( match.arg( arg=method, choices=c("trapezoid","step","spline","linear") )
# , "trapezoid" = { a <- sum((apply( cbind(y[-length(y)], y[-1]), 1, mean))*(x[-1] - x[-length(x)])) }
# , "step" = { a <- sum( y[-length(y)] * (x[-1] - x[-length(x)])) }
# , "linear" = {
# a <- MESS_auc(x, y, from = from , to = to, type="linear",
# absolutearea=absolutearea, subdivisions=subdivisions, ...)
# }
# , "spline" = {
# a <- MESS_auc(x, y, from = from , to = to, type="spline",
# absolutearea=absolutearea, subdivisions=subdivisions, ...)
# # a <- integrate(splinefun(x, y, method="natural"), lower=min(x), upper=max(x))$value
# }
# )
# return(a)
# }
# New version, publish as soon as package sobir is updated
AUC <- function(x, y, from = min(x, na.rm=TRUE), to = max(x, na.rm=TRUE),
method=c("trapezoid", "step", "spline", "linear"), absolutearea = FALSE,
subdivisions = 100, na.rm = FALSE, ...) {
if(identical(method, "linear")){
warning("method linear is no longer supported!")
return(NA)
}
# calculates Area unter the curve
# example:
# AUC( x=c(1,2,3,5), y=c(0,1,1,2))
# AUC( x=c(2,3,4,5), y=c(0,1,1,2))
if(na.rm) {
idx <- complete.cases(cbind(x,y))
x <- x[idx]
y <- y[idx]
}
if (length(x) != length(y))
stop("length x must equal length y")
if (length(x) < 2)
return(NA)
o <- order(x)
x <- x[o]
y <- y[o]
ox <- x[o]
oy <- y[o]
method <- match.arg(method)
if (method=="trapezoid") {
# easy and short
# , "trapezoid" = { a <- sum((apply( cbind(y[-length(y)], y[-1]), 1, mean))*(x[-1] - x[-length(x)])) }
## Default option
if (!absolutearea) {
values <- approx(x, y, xout = sort(unique(c(from, to, x[x > from & x < to]))), ...)
res <- 0.5 * sum(diff(values$x) * (values$y[-1] + values$y[-length(values$y)]))
} else { ## Absolute areas
idx <- which(diff(oy >= 0)!=0)
newx <- c(x, x[idx] - oy[idx]*(x[idx+1]-x[idx]) / (y[idx+1]-y[idx]))
newy <- c(y, rep(0, length(idx)))
values <- approx(newx, newy, xout = sort(unique(c(from, to, newx[newx > from & newx < to]))), ...)
res <- 0.5 * sum(diff(values$x) * (abs(values$y[-1]) + abs(values$y[-length(values$y)])))
}
} else if (method=="step") {
# easy and short
# , "step" = { a <- sum( y[-length(y)] * (x[-1] - x[-length(x)])) }
## Default option
if (!absolutearea) {
values <- approx(x, y, xout = sort(unique(c(from, to, x[x > from & x < to]))), ...)
res <- sum(diff(values$x) * values$y[-length(values$y)])
# res <- sum( y[-length(y)] * (x[-1] - x[-length(x)]))
} else { ## Absolute areas
idx <- which(diff(oy >= 0)!=0)
newx <- c(x, x[idx] - oy[idx]*(x[idx+1]-x[idx]) / (y[idx+1]-y[idx]))
newy <- c(y, rep(0, length(idx)))
values <- approx(newx, newy, xout = sort(unique(c(from, to, newx[newx > from & newx < to]))), ...)
res <- sum(diff(values$x) * abs(values$y[-length(values$y)]))
}
} else if (method=="spline") {
if (absolutearea)
myfunction <- function(z) { abs(splinefun(x, y, method="natural")(z)) }
else
myfunction <- splinefun(x, y, method="natural")
res <- integrate(myfunction, lower=from, upper=to, subdivisions=subdivisions)$value
}
return(res)
}
MESS_auc <- function(x, y, from = min(x, na.rm=TRUE), to = max(x, na.rm=TRUE), type=c("linear", "spline"),
absolutearea=FALSE, subdivisions =100, ...) {
type <- match.arg(type)
# Sanity checks
stopifnot(length(x) == length(y))
stopifnot(!is.na(from))
if (length(unique(x)) < 2)
return(NA)
if (type=="linear") {
## Default option
if (absolutearea==FALSE) {
values <- approx(x, y, xout = sort(unique(c(from, to, x[x > from & x < to]))), ...)
res <- 0.5 * sum(diff(values$x) * (values$y[-1] + values$y[-length(values$y)]))
} else { ## Absolute areas
## This is done by adding artificial dummy points on the x axis
o <- order(x)
ox <- x[o]
oy <- y[o]
idx <- which(diff(oy >= 0)!=0)
newx <- c(x, x[idx] - oy[idx]*(x[idx+1]-x[idx]) / (y[idx+1]-y[idx]))
newy <- c(y, rep(0, length(idx)))
values <- approx(newx, newy, xout = sort(unique(c(from, to, newx[newx > from & newx < to]))), ...)
res <- 0.5 * sum(diff(values$x) * (abs(values$y[-1]) + abs(values$y[-length(values$y)])))
}
} else { ## If it is not a linear approximation
if (absolutearea)
myfunction <- function(z) { abs(splinefun(x, y, method="natural")(z)) }
else
myfunction <- splinefun(x, y, method="natural")
res <- integrate(myfunction, lower=from, upper=to, subdivisions=subdivisions)$value
}
res
}
# library(microbenchmark)
#
# baseMode <- function(x, narm = FALSE) {
# if (narm) x <- x[!is.na(x)]
# ux <- unique(x)
# ux[which.max(table(match(x, ux)))]
# }
# x <- round(rnorm(1e7) *100, 4)
# microbenchmark(Mode(x), baseMode(x), DescTools:::fastMode(x), times = 15, unit = "relative")
#
# mode value, the most frequent element
Mode <- function(x, na.rm=FALSE) {
# // Source
# // https://stackoverflow.com/questions/55212746/rcpp-fast-statistical-mode-function-with-vector-input-of-any-type
# // Author: Ralf Stubner, Joseph Wood
if(!is.atomic(x) | is.matrix(x)) stop("Mode supports only atomic vectors. Use sapply(*, Mode) instead.")
if (na.rm)
x <- x[!is.na(x)]
if (anyNA(x))
# there are NAs, so no mode exist nor frequency
return(structure(NA_real_, freq = NA_integer_))
if(length(x) == 1L)
# only one value in x, x is the mode
# return(structure(x, freq = 1L))
# changed to: only one value in x, no mode defined
return(structure(NA_real_, freq = NA_integer_))
# we don't have NAs so far, either there were then we've already stopped
# or they've been stripped above
res <- fastModeX(x, narm=FALSE)
# no mode existing, if max freq is only 1 observation
if(length(res)== 0L & attr(res, "freq")==1L)
return(structure(NA_real_, freq = NA_integer_))
else
# order results kills the attribute
return(structure(res[order(res)], freq = attr(res, "freq")))
}
Gmean <- function (x, method = c("classic", "boot"),
conf.level = NA, sides = c("two.sided","left","right"),
na.rm = FALSE, ...) {
# see also: http://www.stata.com/manuals13/rameans.pdf
if(na.rm) x <- na.omit(x)
if(any(is.na(x) | (is_neg <- x < 0))){
if(any(na.omit(is_neg)))
warning("x contains negative values")
if(is.na(conf.level))
NA
else
c(NA, NA, NA)
} else if(any(x==0)) {
if(is.na(conf.level))
0
else
c(0, NA, NA)
} else {
if(is.na(conf.level))
exp(mean(log(x)))
else
exp(MeanCI(x=log(x), method = method,
conf.level = conf.level, sides = sides, ...))
}
}
Gsd <- function (x, na.rm = FALSE) {
if(na.rm) x <- na.omit(x)
is.na(x) <- x <= 0
exp(sd(log(x)))
}
Hmean <- function(x, method = c("classic", "boot"),
conf.level = NA, sides = c("two.sided","left","right"),
na.rm = FALSE, ...) {
# see also for alternative ci
# https://www.unistat.com/guide/confidence-intervals/
is.na(x) <- x <= 0
if(is.na(conf.level))
res <- 1 / mean(1/x, na.rm = na.rm)
else {
# res <- (1 / MeanCI(x = 1/x, method = method,
# conf.level = conf.level, sides = sides, na.rm=na.rm, ...))
#
# if(!is.na(conf.level)){
# res[2:3] <- c(min(res[2:3]), max(res[2:3]))
# if(res[2] < 0)
# res[c(2,3)] <- NA
# }
#
sides <- match.arg(sides, choices = c("two.sided", "left",
"right"), several.ok = FALSE)
if (sides != "two.sided")
conf.level <- 1 - 2 * (1 - conf.level)
res <- (1/(mci <- MeanCI(x = 1/x, method = method, conf.level = conf.level,
sides = "two.sided", na.rm = na.rm, ...)))[c(1, 3, 2)]
# check if lower ci < 0, if so return NA, as CI not defined see Stata definition
if( mci[2] <= 0)
res[2:3] <- NA
names(res) <- names(res)[c(1,3,2)]
if (sides == "left")
res[3] <- Inf
else if (sides == "right")
# it's not clear, if we should not set this to 0
res[2] <- NA
}
return(res)
}
TukeyBiweight <- function(x, const=9, na.rm = FALSE, conf.level = NA, ci.type = "bca", R=1000, ...) {
if(na.rm) x <- na.omit(x)
if(anyNA(x)) return(NA)
if(is.na(conf.level)){
# .Call("tbrm", as.double(x[!is.na(x)]), const)
res <- .Call("tbrm", PACKAGE="DescTools", as.double(x), const)
} else {
# adjusted bootstrap percentile (BCa) interval
boot.tbw <- boot(x, function(x, d) .Call("tbrm", PACKAGE="DescTools", as.double(x[d]), const), R=R, ...)
ci <- boot.ci(boot.tbw, conf=conf.level, type=ci.type)
res <- c(tbw=boot.tbw$t0, lwr.ci=ci[[4]][4], upr.ci=ci[[4]][5])
}
return(res)
}
## Originally from /u/ftp/NDK/Source-NDK-9/R/rg2-fkt.R :
.tauHuber <- function(x, mu, k=1.345, s = mad(x), resid = (x - mu)/s) {
## Purpose: Korrekturfaktor Tau fuer die Varianz von Huber-M-Schaetzern
## -------------------------------------------------------------------------
## Arguments: x = Daten mu = Lokations-Punkt k = Parameter der Huber Psi-Funktion
## -------------------------------------------------------------------------
## Author: Rene Locher Update: R. Frisullo 23.4.02; M.Maechler (as.log(); s, resid)
inr <- abs(resid) <= k
psi <- ifelse(inr, resid, sign(resid)*k) # psi (x)
psiP <- as.logical(inr)# = ifelse(abs(resid) <= k, 1, 0) # psi'(x)
length(x) * sum(psi^2) / sum(psiP)^2
}
.wgt.himedian <- function(x, weights = rep(1,n)) {
# Purpose: weighted hiMedian of x
# Author: Martin Maechler, Date: 14 Mar 2002
n <- length(x <- as.double(x))
stopifnot(storage.mode(weights) %in% c("integer", "double"))
if(n != length(weights))
stop("'weights' must have same length as 'x'")
# if(is.integer(weights)) message("using integer weights")
# Original
# .C(if(is.integer(weights)) "wgt_himed_i" else "wgt_himed",
# x, n, weights,
# res = double(1))$res
if(is.integer(weights))
.C("wgt_himed_i",
x, n, weights,
res = double(1))$res
else
.C("wgt_himed",
x, n, weights,
res = double(1))$res
}
## A modified "safe" (and more general) Huber estimator:
.huberM <-
function(x, k = 1.345, weights = NULL,
tol = 1e-06,
mu = if(is.null(weights)) median(x) else .wgt.himedian(x, weights),
s = if(is.null(weights)) mad(x, center=mu)
else .wgt.himedian(abs(x - mu), weights),
se = FALSE,
warn0scale = getOption("verbose"))
{
## Author: Martin Maechler, Date: 6 Jan 2003, ff
## implicit 'na.rm = TRUE':
if(any(i <- is.na(x))) {
x <- x[!i]
if(!is.null(weights)) weights <- weights[!i]
}
n <- length(x)
sum.w <-
if(!is.null(weights)) {
stopifnot(is.numeric(weights), weights >= 0, length(weights) == n)
sum(weights)
} else n
it <- 0L
NA. <- NA_real_
if(sum.w == 0) # e.g 'x' was all NA
return(list(mu = NA., s = NA., it = it, se = NA.)) # instead of error
if(se && !is.null(weights))
stop("Std.error computation not yet available for the case of 'weights'")
if (s <= 0) {
if(s < 0) stop("negative scale 's'")
if(warn0scale && n > 1)
warning("scale 's' is zero -- returning initial 'mu'")
}
else {
wsum <- if(is.null(weights)) sum else function(u) sum(u * weights)
repeat {
it <- it + 1L
y <- pmin(pmax(mu - k * s, x), mu + k * s)
mu1 <- wsum(y) / sum.w
if (abs(mu - mu1) < tol * s)
break
mu <- mu1
}
}
list(mu = mu, s = s, it = it,
SE = if(se) s * sqrt(.tauHuber(x, mu=mu, s=s, k=k) / n) else NA.)
}
HuberM <- function(x, k = 1.345, mu = median(x), s = mad(x, center=mu),
na.rm = FALSE, conf.level = NA, ci.type = c("wald", "boot"), ...){
# new interface to HuberM, making it less complex
# refer to robustbase::huberM if more control is required
if(na.rm) x <- na.omit(x)
if(anyNA(x)) return(NA)
if(is.na(conf.level)){
res <- .huberM(x=x, k=k, mu=mu, s=s, warn0scale=TRUE)$mu
return(res)
} else {
switch(match.arg(ci.type)
,"wald"={
res <- .huberM(x=x, k=k, mu=mu, s=s, se=TRUE, warn0scale=TRUE)
# Solution: (12.6.06) - Robuste Regression (Rg-2d) - Musterloeungen zu Serie 1
# r.loc$mu + c(-1,1)*qt(0.975,8)*sqrt(t.tau/length(d.ertrag))*r.loc$s
#
# Ruckstuhl's Loesung:
# (Sleep.HM$mu + c(-1,1)*qt(0.975, length(Sleep)-1) *
# sqrt(f.tau(Sleep, Sleep.HM$mu)) * Sleep.HM$s/sqrt(length(Sleep)))
# ci <- qnorm(1-(1-conf.level)/2) * res$SE
ci <- qt(1-(1-conf.level)/2, length(x)-1) *
sqrt(.tauHuber(x, res$mu, k=k)) * res$s/sqrt(length(x))
res <- c(hm=res$mu, lwr.ci=res$mu - ci, upr.ci=res$mu + ci)
}
,"boot" ={
R <- InDots(..., arg="R", default=1000)
bci.type <- InDots(..., arg="type", default="perc")
boot.hm <- boot(x, function(x, d){
hm <- .huberM(x=x[d], k=k, mu=mu, s=s, se=TRUE)
return(c(hm$mu, hm$s^2))
}, R=R)
ci <- boot.ci(boot.hm, conf=conf.level, ...)
if(ci.type =="norm") {
lwr.ci <- ci[[4]][2]
upr.ci <- ci[[4]][3]
} else {
lwr.ci <- ci[[4]][4]
upr.ci <- ci[[4]][5]
}
res <- c(hm=boot.hm$t0[1], lwr.ci=lwr.ci, upr.ci=upr.ci)
}
)
return(res)
}
}
# old version, replace 13.5.2015
#
# # A modified "safe" (and more general) Huber estimator:
# HuberM <- function(x, k = 1.5, weights = NULL, tol = 1e-06,
# mu = if(is.null(weights)) median(x) else wgt.himedian(x, weights),
# s = if(is.null(weights)) mad(x, center=mu) else wgt.himedian(abs(x - mu), weights),
# se = FALSE, warn0scale = getOption("verbose"), na.rm = FALSE, stats = FALSE) {
#
# # Author: Martin Maechler, Date: 6 Jan 2003, ff
#
# # Originally from /u/ftp/NDK/Source-NDK-9/R/rg2-fkt.R :
# tauHuber <- function(x, mu, k=1.5, s = mad(x), resid = (x - mu)/s) {
# # Purpose: Korrekturfaktor Tau fuer die Varianz von Huber-M-Schaetzern
# # ******************************************************************************
# # Arguments: x = Daten mu = Lokations-Punkt k = Parameter der Huber Psi-Funktion
# # ******************************************************************************
# # Author: Rene Locher Update: R. Frisullo 23.4.02; M.Maechler (as.log(); s, resid)
# inr <- abs(resid) <= k
# psi <- ifelse(inr, resid, sign(resid)*k) #### psi (x)
# psiP <- as.logical(inr) # = ifelse(abs(resid) <= k, 1, 0) #### psi'(x)
# length(x) * sum(psi^2) / sum(psiP)^2
# }
#
# wgt.himedian <- function(x, weights = rep(1,n)) {
#
# # Purpose: weighted hiMedian of x
# # Author: Martin Maechler, Date: 14 Mar 2002
# n <- length(x <- as.double(x))
# stopifnot(storage.mode(weights) %in% c("integer", "double"))
# if(n != length(weights))
# stop("'weights' must have same length as 'x'")
# # if(is.integer(weights)) message("using integer weights")
# .C(if(is.integer(weights)) "wgt_himed_i" else "wgt_himed",
# x, n, weights,
# res = double(1))$res
# }
#
#
# # Andri: introduce na.rm
# # old: implicit 'na.rm = TRUE'
# if(na.rm) {
# i <- is.na(x)
# x <- x[!i]
# if(!is.null(weights)) weights <- weights[!i]
# } else {
# if(anyNA(x)) return(NA)
# }
#
#
# n <- length(x)
# sum.w <-
# if(!is.null(weights)) {
# stopifnot(is.numeric(weights), weights >= 0, length(weights) == n)
# sum(weights)
# } else n
# it <- 0L
# NA. <- NA_real_
# if(sum.w == 0) # e.g 'x' was all NA
# return(list(mu = NA., s = NA., it = it, se = NA.)) # instead of error
#
# if(se && !is.null(weights))
# stop("Std.error computation not yet available for the case of 'weights'")
#
# if (s <= 0) {
# if(s < 0) stop("negative scale 's'")
# if(warn0scale && n > 1)
# warning("scale 's' is zero -- returning initial 'mu'")
# }
# else {
# wsum <- if(is.null(weights)) sum else function(u) sum(u * weights)
#
# repeat {
# it <- it + 1L
# y <- pmin(pmax(mu - k * s, x), mu + k * s)
# mu1 <- wsum(y) / sum.w
# if (abs(mu - mu1) < tol * s)
# break
# mu <- mu1
# }
# }
#
# if(stats)
# res <- list(mu = mu, s = s, it = it,
# SE = if(se) s * sqrt(tauHuber(x, mu=mu, s=s, k=k) / n) else NA.)
# else
# res <- mu
#
# return(res)
#
# }
HodgesLehmann <- function(x, y = NULL, conf.level = NA, na.rm = FALSE) {
# Werner Stahel's version:
#
# f.HodgesLehmann <- function(data)
# {
# ## Purpose: Hodges-Lehmann estimate and confidence interval
# ## -------------------------------------------------------------------------
# ## Arguments:
# ## Remark: function changed so that CI covers >= 95%, before it was too
# ## small (9/22/04)
# ## -------------------------------------------------------------------------
# ## Author: Werner Stahel, Date: 12 Aug 2002, 14:13
# ## Update: Beat Jaggi, Date: 22 Sept 2004
# .cexact <-
# # c(NA,NA,NA,NA,NA,21,26,33,40,47,56,65,74,84,95,107,119,131,144,158)
# c(NA,NA,NA,NA,NA,22,27,34,41,48,57,66,75,85,96,108,120,132,145,159)
# .d <- na.omit(data)
# .n <- length(.d)
# .wa <- sort(c(outer(.d,.d,"+")/2)[outer(1:.n,1:.n,"<=")])
# .c <- if (.n<=length(.cexact)) .n*(.n+1)/2+1-.cexact[.n] else
# floor(.n*(.n+1)/4-1.96*sqrt(.n*(.n+1)*(2*.n+1)/24))
# .r <- c(median(.wa), .wa[c(.c,.n*(.n+1)/2+1-.c)])
# names(.r) <- c("estimate","lower","upper")
# .r
# }
if(na.rm) {
if(is.null(y))
x <- na.omit(x)
else {
ok <- complete.cases(x, y)
x <- x[ok]
y <- y[ok]
}
}
if(anyNA(x) || (!is.null(y) && anyNA(y)))
if(is.na(conf.level))
return(NA)
else
return(c(est=NA, lwr.ci=NA, upr.ci=NA))
# res <- wilcox.test(x, y, conf.int = TRUE, conf.level = Coalesce(conf.level, 0.8))
if(is.null(y)){
res <- .Call("_DescTools_hlqest", PACKAGE = "DescTools", x)
} else {
res <- .Call("_DescTools_hl2qest", PACKAGE = "DescTools", x, y)
}
if(is.na(conf.level)){
result <- res
names(result) <- NULL
} else {
n <- length(x)
# lci <- n^2/2 + qnorm((1-conf.level)/2) * sqrt(n^2 * (2*n+1)/12) - 0.5
# uci <- n^2/2 - qnorm((1-conf.level)/2) * sqrt(n^2 * (2*n+1)/12) - 0.5
lci <- uci <- NA
warning("Confidence intervals not yet implemented for Hodges-Lehman-Estimator.")
result <- c(est=res, lwr.ci=lci, upr.ci=uci)
}
return(result)
}
Skew <- function (x, weights=NULL, na.rm = FALSE, method = 3, conf.level = NA, ci.type = "bca", R=1000, ...) {
# C part for the expensive (x - mean(x))^2 etc. is a kind of 14 times faster
# > x <- rchisq(100000000, df=2)
# > system.time(Skew(x))
# user system elapsed
# 6.32 0.30 6.62
# > system.time(Skew2(x))
# user system elapsed
# 0.47 0.00 0.47
i.skew <- function(x, weights=NULL, method = 3) {
# method 1: older textbooks
if(!is.null(weights)){
# use a standard treatment for weights
z <- .NormWeights(x, weights, na.rm=na.rm, zero.rm=TRUE)
r.skew <- .Call("rskeww",
as.numeric(z$x), as.numeric(Mean(z$x, weights = z$weights)),
as.numeric(z$weights), PACKAGE="DescTools")
n <- z$wsum
} else {
if (na.rm) x <- na.omit(x)
r.skew <- .Call("rskew", as.numeric(x),
as.numeric(mean(x)), PACKAGE="DescTools")
n <- length(x)
}
se <- sqrt((6*(n-2))/((n+1)*(n+3)))
if (method == 2) {
# method 2: SAS/SPSS
r.skew <- r.skew * n^0.5 * (n - 1)^0.5/(n - 2)
se <- se * sqrt(n*(n-1))/(n-2)
}
else if (method == 3) {
# method 3: MINITAB/BDMP
r.skew <- r.skew * ((n - 1)/n)^(3/2)
se <- se * ((n - 1)/n)^(3/2)
}
return(c(r.skew, se^2))
}
if(is.na(conf.level)){
res <- i.skew(x, weights=weights, method=method)[1]
} else {
if(ci.type == "classic") {
res <- i.skew(x, weights=weights, method=method)
res <- c(skewness=res[1],
lwr.ci=qnorm((1-conf.level)/2) * sqrt(res[2]),
upr.ci=qnorm(1-(1-conf.level)/2) * sqrt(res[2]))
} else {
# Problematic standard errors and confidence intervals for skewness and kurtosis.
# Wright DB, Herrington JA. (2011) recommend only bootstrap intervals
# adjusted bootstrap percentile (BCa) interval
boot.skew <- boot(x, function(x, d) i.skew(x[d], weights=weights, method=method), R=R, ...)
ci <- boot.ci(boot.skew, conf=conf.level, type=ci.type)
if(ci.type =="norm") {
lwr.ci <- ci[[4]][2]
upr.ci <- ci[[4]][3]
} else {
lwr.ci <- ci[[4]][4]
upr.ci <- ci[[4]][5]
}
res <- c(skew=boot.skew$t0[1], lwr.ci=lwr.ci, upr.ci=upr.ci)
}
}
return(res)
}
Kurt <- function (x, weights=NULL, na.rm = FALSE, method = 3, conf.level = NA,
ci.type = "bca", R=1000, ...) {
i.kurt <- function(x, weights=NULL, na.rm = FALSE, method = 3) {
# method 1: older textbooks
if(!is.null(weights)){
# use a standard treatment for weights
z <- .NormWeights(x, weights, na.rm=na.rm, zero.rm=TRUE)
r.kurt <- .Call("rkurtw", as.numeric(z$x), as.numeric(Mean(z$x, weights = z$weights)), as.numeric(z$weights), PACKAGE="DescTools")
n <- z$wsum
} else {
if (na.rm) x <- na.omit(x)
r.kurt <- .Call("rkurt", as.numeric(x), as.numeric(mean(x)), PACKAGE="DescTools")
n <- length(x)
}
se <- sqrt((24*n*(n-2)*(n-3))/((n+1)^2*(n+3)*(n+5)))
if (method == 2) {
# method 2: SAS/SPSS
r.kurt <- ((r.kurt + 3) * (n + 1)/(n - 1) - 3) * (n - 1)^2/(n - 2)/(n - 3)
se <- se * (((n-1)*(n+1))/((n-2)*(n-3)))
}
else if (method == 3) {
# method 3: MINITAB/BDMP
r.kurt <- (r.kurt + 3) * (1 - 1/n)^2 - 3
se <- se * ((n-1)/n)^2
}
return(c(r.kurt, se^2))
}
if(is.na(conf.level)){
res <- i.kurt(x, weights=weights, na.rm=na.rm, method=method)[1]
} else {
if(ci.type == "classic") {
res <- i.kurt(x, weights=weights, method=method)
res <- c(kurtosis=res[1],
lwr.ci=qnorm((1-conf.level)/2) * sqrt(res[2]),
upr.ci=qnorm(1-(1-conf.level)/2) * sqrt(res[2]))
} else {
# Problematic standard errors and confidence intervals for skewness and kurtosis.
# Wright DB, Herrington JA. (2011) recommend only bootstrap intervals
# adjusted bootstrap percentile (BCa) interval
boot.kurt <- boot(x, function(x, d) i.kurt(x[d], weights=weights, na.rm=na.rm, method=method), R=R, ...)
ci <- boot.ci(boot.kurt, conf=conf.level, type=ci.type)
if(ci.type =="norm") {
lwr.ci <- ci[[4]][2]
upr.ci <- ci[[4]][3]
} else {
lwr.ci <- ci[[4]][4]
upr.ci <- ci[[4]][5]
}
res <- c(kurt=boot.kurt$t0[1], lwr.ci=lwr.ci, upr.ci=upr.ci)
}
}
return(res)
}
Outlier <- function(x, method=c("boxplot", "hampel"), value=TRUE, na.rm=FALSE){
switch(match.arg(arg = method, choices = c("boxplot", "hampel")),
# boxplot = { x[x %)(% (quantile(x, c(0.25,0.75), na.rm=na.rm) + c(-1,1) * 1.5*IQR(x,na.rm=na.rm))] }
boxplot = {
# old, replaced by v. 0.99.26
# res <- boxplot(x, plot = FALSE)$out
qq <- quantile(as.numeric(x), c(0.25, 0.75), na.rm = na.rm, names = FALSE)
iqr <- diff(qq)
id <- x < (qq[1] - 1.5 * iqr) | x > (qq[2] + 1.5 * iqr)
},
hampel = {
# hampel considers values outside of median ± 3*(median absolute deviation) to be outliers
id <- x %][% (median(x, na.rm=na.rm) + 3 * c(-1, 1) * mad(x, na.rm=na.rm))
}
)
if(value)
res <- x[id]
else
res <- which(id)
res <- res[!is.na(res)]
return(res)
}
LOF <- function(data,k) {
# source: library(dprep)
# A function that finds the local outlier factor (Breunig,2000) of
# the matrix "data" with k neighbors
# Adapted by Caroline Rodriguez and Edgar Acuna, may 2004
knneigh.vect <-
function(x,data,k)
{
#Function that returns the distance from a vector "x" to
#its k-nearest-neighbors in the matrix "data"
temp=as.matrix(data)
numrow=dim(data)[1]
dimnames(temp)=NULL
#subtract rowvector x from each row of data
difference<- scale(temp, x, FALSE)
#square and add all differences and then take the square root
dtemp <- drop(difference^2 %*% rep(1, ncol(data)))
dtemp=sqrt(dtemp)
#order the distances
order.dist <- order(dtemp)
nndist=dtemp[order.dist]
#find distance to k-nearest neighbor
#uses k+1 since first distance in vector is a 0
knndist=nndist[k+1]
#find neighborhood
#eliminate first row of zeros from neighborhood
neighborhood=drop(nndist[nndist<=knndist])
neighborhood=neighborhood[-1]
numneigh=length(neighborhood)
#find indexes of each neighbor in the neighborhood
index.neigh=order.dist[1:numneigh+1]
# this will become the index of the distance to first neighbor
num1=length(index.neigh)+3
# this will become the index of the distance to last neighbor
num2=length(index.neigh)+numneigh+2
#form a vector
neigh.dist=c(num1,num2,index.neigh,neighborhood)
return(neigh.dist)
}
dist.to.knn <-
function(dataset,neighbors)
{
#function returns an object in which each column contains
#the indices of the first k neighbors followed by the
#distances to each of these neighbors
numrow=dim(dataset)[1]
#applies a function to find distance to k nearest neighbors
#within "dataset" for each row of the matrix "dataset"
knndist=rep(0,0)
for (i in 1:numrow)
{
#find obervations that make up the k-distance neighborhood for dataset[i,]
neighdist=knneigh.vect(dataset[i,],dataset,neighbors)
#adjust the length of neighdist or knndist as needed to form matrix of neighbors
#and their distances
if (i==2)
{
if (length(knndist)<length(neighdist))
{
z=length(neighdist)-length(knndist)
zeros=rep(0,z)
knndist=c(knndist,zeros)
}
else if (length(knndist)>length(neighdist))
{
z=length(knndist)-length(neighdist)
zeros=rep(0,z)
neighdist=c(neighdist,zeros)
}
}
else
{
if (i!=1)
{
if (dim(knndist)[1]<length(neighdist))
{
z=(length(neighdist)-dim(knndist)[1])
zeros=rep(0,z*dim(knndist)[2])
zeros=matrix(zeros,z,dim(knndist)[2])
knndist=rbind(knndist,zeros)
}
else if (dim(knndist)[1]>length(neighdist))
{
z=(dim(knndist)[1]-length(neighdist))
zeros=rep(0,z)
neighdist=c(neighdist,zeros)
}
}
}
knndist=cbind(knndist,neighdist)
}
return(knndist)
}
reachability <-
function(distdata,k)
{
# function that calculates the local reachability density
# of Breuing(2000) for each observation in a matrix, using
# a matrix (distdata) of k nearest neighbors computed by the function dist.to.knn2
p=dim(distdata)[2]
lrd=rep(0,p)
for (i in 1:p)
{
j=seq(3,3+(distdata[2,i]-distdata[1,i]))
# compare the k-distance from each observation to its kth neighbor
# to the actual distance between each observation and its neighbors
numneigh=distdata[2,i]-distdata[1,i]+1
temp=rbind(diag(distdata[distdata[2,distdata[j,i]],distdata[j,i]]),distdata[j+numneigh,i])
# calculate reachability
reach=1/(sum(apply(temp,2,max))/numneigh)
lrd[i]=reach
}
lrd
}
data=as.matrix(data)
# find k nearest neighbors and their distance from each observation
# in data
distdata=dist.to.knn(data,k)
p=dim(distdata)[2]
# calculate the local reachability density for each observation in data
lrddata=reachability(distdata,k)
lof=rep(0,p)
# computer the local outlier factor of each observation in data
for ( i in 1:p)
{
nneigh=distdata[2,i]-distdata[1,i]+1
j=seq(0,(nneigh-1))
local.factor=sum(lrddata[distdata[3+j,i]]/lrddata[i])/nneigh
lof[i]=local.factor
}
# return lof, a vector with the local outlier factor of each observation
lof
}
BootCI <- function(x, y=NULL, FUN, ..., bci.method = c("norm", "basic", "stud", "perc", "bca"),
conf.level = 0.95, sides = c("two.sided", "left", "right"), R = 999) {
dots <- as.list(substitute(list( ... ))) [-1]
bci.method <- match.arg(bci.method)
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
if(is.null(y))
boot.fun <- boot(x, function(x, d) do.call(FUN, append(list(x[d]), dots)), R = R)
else
boot.fun <- boot(x, function(x, d) do.call(FUN, append(list(x[d], y[d]), dots)), R = R)
ci <- boot.ci(boot.fun, conf=conf.level, type=bci.method)
if (bci.method == "norm") {
res <- c(est = boot.fun$t0, lwr.ci = ci[[4]][2],
upr.ci = ci[[4]][3])
} else {
res <- c(est = boot.fun$t0, lwr.ci = ci[[4]][4],
upr.ci = ci[[4]][5])
}
if(sides=="left")
res[3] <- Inf
else if(sides=="right")
res[2] <- -Inf
names(res)[1] <- deparse(substitute(FUN))
return(res)
}
# Confidence Intervals for Binomial Proportions
BinomCI <- function(x, n, conf.level = 0.95, sides = c("two.sided","left","right"),
method = c("wilson", "wald", "waldcc", "agresti-coull", "jeffreys", "modified wilson", "wilsoncc",
"modified jeffreys", "clopper-pearson", "arcsine", "logit", "witting", "pratt", "midp", "lik", "blaker"),
rand = 123, tol=1e-05, std_est=TRUE) {
if(missing(method)) method <- "wilson"
if(missing(sides)) sides <- "two.sided"
iBinomCI <- function(x, n, conf.level = 0.95, sides = c("two.sided","left","right"),
method = c("wilson", "wilsoncc", "wald", "waldcc","agresti-coull", "jeffreys", "modified wilson",
"modified jeffreys", "clopper-pearson", "arcsine", "logit", "witting", "pratt", "midp", "lik", "blaker"),
rand = 123, tol=1e-05, std_est=TRUE) {
if(length(x) != 1) stop("'x' has to be of length 1 (number of successes)")
if(length(n) != 1) stop("'n' has to be of length 1 (number of trials)")
if(length(conf.level) != 1) stop("'conf.level' has to be of length 1 (confidence level)")
if(conf.level < 0.5 | conf.level > 1) stop("'conf.level' has to be in [0.5, 1]")
method <- match.arg(arg=method,
choices=c("wilson", "wald", "waldcc", "wilsoncc","agresti-coull",
"jeffreys", "modified wilson",
"modified jeffreys", "clopper-pearson", "arcsine",
"logit", "witting","pratt", "midp", "lik", "blaker"))
sides <- match.arg(sides, choices = c("two.sided","left","right"),
several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
alpha <- 1 - conf.level
kappa <- qnorm(1-alpha/2)
p.hat <- x/n
q.hat <- 1 - p.hat
# this is the default estimator used by the most (but not all) methods
est <- p.hat
switch( method
, "wald" = {
term2 <- kappa*sqrt(p.hat*q.hat)/sqrt(n)
CI.lower <- max(0, p.hat - term2)
CI.upper <- min(1, p.hat + term2)
}
, "waldcc" = {
term2 <- kappa*sqrt(p.hat*q.hat)/sqrt(n)
# continuity correction
term2 <- term2 + 1/(2*n)
CI.lower <- max(0, p.hat - term2)
CI.upper <- min(1, p.hat + term2)
}
, "wilson" = {
# non standard estimator
if(!std_est){
x.tilde <- x + kappa^2/2
n.tilde <- n + kappa^2
p.tilde <- x.tilde/n.tilde
est <- p.tilde
}
term1 <- (x + kappa^2/2)/(n + kappa^2)
term2 <- kappa*sqrt(n)/(n + kappa^2)*sqrt(p.hat*q.hat + kappa^2/(4*n))
CI.lower <- max(0, term1 - term2)
CI.upper <- min(1, term1 + term2)
}
, "wilsoncc" = {
# non standard estimator
if(!std_est){
x.tilde <- x + kappa^2/2
n.tilde <- n + kappa^2
p.tilde <- x.tilde/n.tilde
est <- p.tilde
}
lci <- ( 2*x+kappa**2 -1 - kappa*sqrt(kappa**2 -
2- 1/n + 4*p.hat*(n*q.hat+1))) / (2*(n+kappa**2))
uci <- ( 2*x+kappa**2 +1 + kappa*sqrt(kappa**2 +
2- 1/n + 4*p.hat*(n*q.hat-1))) / (2*(n+kappa**2))
CI.lower <- max(0, ifelse(p.hat==0, 0, lci))
CI.upper <- min(1, ifelse(p.hat==1, 1, uci))
}
, "agresti-coull" = {
x.tilde <- x + kappa^2/2
n.tilde <- n + kappa^2
p.tilde <- x.tilde/n.tilde
q.tilde <- 1 - p.tilde
# non standard estimator
if(!std_est)
est <- p.tilde
term2 <- kappa*sqrt(p.tilde*q.tilde)/sqrt(n.tilde)
CI.lower <- max(0, p.tilde - term2)
CI.upper <- min(1, p.tilde + term2)
}
, "jeffreys" = {
if(x == 0)
CI.lower <- 0
else
CI.lower <- qbeta(alpha/2, x+0.5, n-x+0.5)
if(x == n)
CI.upper <- 1
else
CI.upper <- qbeta(1-alpha/2, x+0.5, n-x+0.5)
}
, "modified wilson" = {
# non standard estimator
if(!std_est){
x.tilde <- x + kappa^2/2
n.tilde <- n + kappa^2
p.tilde <- x.tilde/n.tilde
est <- p.tilde
}
term1 <- (x + kappa^2/2)/(n + kappa^2)
term2 <- kappa*sqrt(n)/(n + kappa^2)*sqrt(p.hat*q.hat + kappa^2/(4*n))
## comment by Andre Gillibert, 19.6.2017:
## old:
## if((n <= 50 & x %in% c(1, 2)) | (n >= 51 & n <= 100 & x %in% c(1:3)))
## new:
if((n <= 50 & x %in% c(1, 2)) | (n >= 51 & x %in% c(1:3)))
CI.lower <- 0.5*qchisq(alpha, 2*x)/n
else
CI.lower <- max(0, term1 - term2)
## if((n <= 50 & x %in% c(n-1, n-2)) | (n >= 51 & n <= 100 & x %in% c(n-(1:3))))
if((n <= 50 & x %in% c(n-1, n-2)) | (n >= 51 & x %in% c(n-(1:3))))
CI.upper <- 1 - 0.5*qchisq(alpha, 2*(n-x))/n
else
CI.upper <- min(1, term1 + term2)
}
, "modified jeffreys" = {
if(x == n)
CI.lower <- (alpha/2)^(1/n)
else {
if(x <= 1)
CI.lower <- 0
else
CI.lower <- qbeta(alpha/2, x+0.5, n-x+0.5)
}
if(x == 0)
CI.upper <- 1 - (alpha/2)^(1/n)
else{
if(x >= n-1)
CI.upper <- 1
else
CI.upper <- qbeta(1-alpha/2, x+0.5, n-x+0.5)
}
}
, "clopper-pearson" = {
CI.lower <- qbeta(alpha/2, x, n-x+1)
CI.upper <- qbeta(1-alpha/2, x+1, n-x)
}
, "arcsine" = {
p.tilde <- (x + 0.375)/(n + 0.75)
# non standard estimator
if(!std_est)
est <- p.tilde
CI.lower <- sin(asin(sqrt(p.tilde)) - 0.5*kappa/sqrt(n))^2
CI.upper <- sin(asin(sqrt(p.tilde)) + 0.5*kappa/sqrt(n))^2
}
, "logit" = {
lambda.hat <- log(x/(n-x))
V.hat <- n/(x*(n-x))
lambda.lower <- lambda.hat - kappa*sqrt(V.hat)
lambda.upper <- lambda.hat + kappa*sqrt(V.hat)
CI.lower <- exp(lambda.lower)/(1 + exp(lambda.lower))
CI.upper <- exp(lambda.upper)/(1 + exp(lambda.upper))
}
, "witting" = {
set.seed(rand)
x.tilde <- x + runif(1, min = 0, max = 1)
pbinom.abscont <- function(q, size, prob){
v <- trunc(q)
term1 <- pbinom(v-1, size = size, prob = prob)
term2 <- (q - v)*dbinom(v, size = size, prob = prob)
return(term1 + term2)
}
qbinom.abscont <- function(p, size, x){
fun <- function(prob, size, x, p){
pbinom.abscont(x, size, prob) - p
}
uniroot(fun, interval = c(0, 1), size = size, x = x, p = p)$root
}
CI.lower <- qbinom.abscont(1-alpha, size = n, x = x.tilde)
CI.upper <- qbinom.abscont(alpha, size = n, x = x.tilde)
}
, "pratt" = {
if(x==0) {
CI.lower <- 0
CI.upper <- 1-alpha^(1/n)
} else if(x==1) {
CI.lower <- 1-(1-alpha/2)^(1/n)
CI.upper <- 1-(alpha/2)^(1/n)
} else if(x==(n-1)) {
CI.lower <- (alpha/2)^(1/n)
CI.upper <- (1-alpha/2)^(1/n)
} else if(x==n) {
CI.lower <- alpha^(1/n)
CI.upper <- 1
} else {
z <- qnorm(1 - alpha/2)
A <- ((x+1) / (n-x))^2
B <- 81*(x+1)*(n-x)-9*n-8
C <- (0-3)*z*sqrt(9*(x+1)*(n-x)*(9*n+5-z^2)+n+1)
D <- 81*(x+1)^2-9*(x+1)*(2+z^2)+1
E <- 1+A*((B+C)/D)^3
CI.upper <- 1/E
A <- (x / (n-x-1))^2
B <- 81*x*(n-x-1)-9*n-8
C <- 3*z*sqrt(9*x*(n-x-1)*(9*n+5-z^2)+n+1)
D <- 81*x^2-9*x*(2+z^2)+1
E <- 1+A*((B+C)/D)^3
CI.lower <- 1/E
}
}
, "midp" = {
#Functions to find root of for the lower and higher bounds of the CI
#These are helper functions.
f.low <- function(pi, x, n) {
1/2*dbinom(x, size=n, prob=pi) +
pbinom(x, size=n, prob=pi, lower.tail=FALSE) - (1-conf.level)/2
}
f.up <- function(pi, x, n) {
1/2*dbinom(x, size=n, prob=pi) +
pbinom(x-1, size=n, prob=pi) - (1-conf.level)/2
}
# One takes pi_low = 0 when x=0 and pi_up=1 when x=n
CI.lower <- 0
CI.upper <- 1
# Calculate CI by finding roots of the f funcs
if (x!=0) {
CI.lower <- uniroot(f.low, interval=c(0, p.hat), x=x, n=n)$root
}
if (x!=n) {
CI.upper <- uniroot(f.up, interval=c(p.hat, 1), x=x, n=n)$root
}
}
, "lik" = {
CI.lower <- 0
CI.upper <- 1
z <- qnorm(1 - alpha * 0.5)
# preset tolerance, should we offer function argument?
tol = .Machine$double.eps^0.5
BinDev <- function(y, x, mu, wt, bound = 0,
tol = .Machine$double.eps^0.5, ...) {
# returns the binomial deviance for y, x, wt
ll.y <- ifelse(y %in% c(0, 1), 0, dbinom(x, wt, y, log=TRUE))
ll.mu <- ifelse(mu %in% c(0, 1), 0, dbinom(x, wt, mu, log=TRUE))
res <- ifelse(abs(y - mu) < tol, 0,
sign(y - mu) * sqrt(-2 * (ll.y - ll.mu)))
return(res - bound)
}
if(x!=0 && tol<p.hat) {
CI.lower <- if(BinDev(tol, x, p.hat, n, -z, tol) <= 0) {
uniroot(f = BinDev, interval = c(tol, if(p.hat < tol || p.hat == 1) 1 - tol else p.hat),
bound = -z, x = x, mu = p.hat, wt = n)$root }
}
if(x!=n && p.hat<(1-tol)) {
CI.upper <- if(BinDev(y = 1 - tol, x = x, mu = ifelse(p.hat > 1 - tol, tol, p.hat),
wt = n, bound = z, tol = tol) < 0) {
CI.lower <- if(BinDev(tol, x, if(p.hat < tol || p.hat == 1) 1 - tol else p.hat, n, -z, tol) <= 0) {
uniroot(f = BinDev, interval = c(tol, p.hat),
bound = -z, x = x, mu = p.hat, wt = n)$root }
} else {
uniroot(f = BinDev, interval = c(if(p.hat > 1 - tol) tol else p.hat, 1 - tol),
bound = z, x = x, mu = p.hat, wt = n)$root }
}
}
, "blaker" ={
acceptbin <- function (x, n, p) {
p1 <- 1 - pbinom(x - 1, n, p)
p2 <- pbinom(x, n, p)
a1 <- p1 + pbinom(qbinom(p1, n, p) - 1, n, p)
a2 <- p2 + 1 - pbinom(qbinom(1 - p2, n, p), n, p)
return(min(a1, a2))
}
CI.lower <- 0
CI.upper <- 1
if (x != 0) {
CI.lower <- qbeta((1 - conf.level)/2, x, n - x + 1)
while (acceptbin(x, n, CI.lower + tol) < (1 - conf.level))
CI.lower = CI.lower + tol
}
if (x != n) {
CI.upper <- qbeta(1 - (1 - conf.level)/2, x + 1, n - x)
while (acceptbin(x, n, CI.upper - tol) < (1 - conf.level))
CI.upper <- CI.upper - tol
}
}
)
# dot not return ci bounds outside [0,1]
ci <- c( est=est, lwr.ci=max(0, CI.lower), upr.ci=min(1, CI.upper) )
if(sides=="left")
ci[3] <- 1
else if(sides=="right")
ci[2] <- 0
return(ci)
}
# handle vectors
# which parameter has the highest dimension
lst <- list(x=x, n=n, conf.level=conf.level, sides=sides,
method=method, rand=rand, std_est=std_est)
maxdim <- max(unlist(lapply(lst, length)))
# recycle all params to maxdim
lgp <- lapply( lst, rep, length.out=maxdim )
# # increase conf.level for one sided intervals
# lgp$conf.level[lgp.sides!="two.sided"] <- 1 - 2*(1-lgp$conf.level[lgp.sides!="two.sided"])
# get rownames
lgn <- DescTools::Recycle(x=if(is.null(names(x))) paste("x", seq_along(x), sep=".") else names(x),
n=if(is.null(names(n))) paste("n", seq_along(n), sep=".") else names(n),
conf.level=conf.level, sides=sides, method=method, std_est=std_est)
xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) length(unique(x)) != 1)]), 1, paste, collapse=":")
res <- t(sapply(1:maxdim, function(i) iBinomCI(x=lgp$x[i], n=lgp$n[i],
conf.level=lgp$conf.level[i],
sides=lgp$sides[i],
method=lgp$method[i], rand=lgp$rand[i],
std_est=lgp$std_est[i])))
colnames(res)[1] <- c("est")
rownames(res) <- xn
# if(nrow(res)==1)
# res <- res[1,]
return(res)
}
BinomCIn <- function(p=0.5, width, interval=c(1, 1e5), conf.level=0.95, sides="two.sided", method="wilson") {
uniroot(f = function(n) diff(BinomCI(x=p*n, n=n, conf.level=conf.level,
sides=sides, method=method)[-1]) - width,
interval = interval)$root
}
BinomDiffCI <- function(x1, n1, x2, n2, conf.level = 0.95, sides = c("two.sided","left","right"),
method=c("ac", "wald", "waldcc", "score", "scorecc", "mn",
"mee", "blj", "ha", "hal", "jp")) {
if(missing(sides)) sides <- match.arg(sides)
if(missing(method)) method <- match.arg(method)
iBinomDiffCI <- function(x1, n1, x2, n2, conf.level, sides, method) {
# .Wald #1
# .Wald (Corrected) #2
# .Exact
# .Exact (FM Score)
# .Newcombe Score #10
# .Newcombe Score (Corrected) #11
# .Farrington-Manning
# .Hauck-Anderson
# http://www.jiangtanghu.com/blog/2012/09/23/statistical-notes-5-confidence-intervals-for-difference-between-independent-binomial-proportions-using-sas/
# Interval estimation for the difference between independent proportions: comparison of eleven methods.
# https://www.lexjansen.com/wuss/2016/127_Final_Paper_PDF.pdf
# http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.633.9380&rep=rep1&type=pdf
# Newcombe (1998) (free):
# http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.408.7354&rep=rep1&type=pdf
# no need to check args here, they're already ok...
# method <- match.arg(arg = method,
# choices = c("wald", "waldcc", "ac", "score", "scorecc", "mn",
# "mee", "blj", "ha", "hal", "jp"))
#
# sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
alpha <- 1 - conf.level
kappa <- qnorm(1 - alpha/2)
p1.hat <- x1/n1
p2.hat <- x2/n2
est <- p1.hat - p2.hat
switch(method,
"wald" = {
vd <- p1.hat*(1-p1.hat)/n1 + p2.hat*(1-p2.hat)/n2
term2 <- kappa * sqrt(vd)
CI.lower <- max(-1, est - term2)
CI.upper <- min(1, est + term2)
},
"waldcc" = {
vd <- p1.hat*(1-p1.hat)/n1 + p2.hat*(1-p2.hat)/n2
term2 <- kappa * sqrt(vd)
term2 <- term2 + 0.5 * (1/n1+1/n2)
CI.lower <- max(-1, est - term2)
CI.upper <- min(1, est + term2)
},
"ac" = { # Agresti-Caffo
n1 <- n1+2
n2 <- n2+2
x1 <- x1+1
x2 <- x2+1
p1.hat <- x1/n1
p2.hat <- x2/n2
est1 <- p1.hat - p2.hat
vd <- p1.hat*(1-p1.hat)/n1 + p2.hat*(1-p2.hat)/n2
term2 <- kappa * sqrt(vd)
CI.lower <- max(-1, est1 - term2)
CI.upper <- min(1, est1 + term2)
} ,
"exact" = { # exact
CI.lower <- NA
CI.upper <- NA
},
"score" = { # Newcombe
w1 <- BinomCI(x=x1, n=n1, conf.level=conf.level, method="wilson")
w2 <- BinomCI(x=x2, n=n2, conf.level=conf.level, method="wilson")
l1 <- w1[2]
u1 <- w1[3]
l2 <- w2[2]
u2 <- w2[3]
CI.lower <- est - kappa * sqrt(l1*(1-l1)/n1 + u2*(1-u2)/n2)
CI.upper <- est + kappa * sqrt(u1*(1-u1)/n1 + l2*(1-l2)/n2)
},
"scorecc" = { # Newcombe
w1 <- BinomCI(x=x1, n=n1, conf.level=conf.level, method="wilsoncc")
w2 <- BinomCI(x=x2, n=n2, conf.level=conf.level, method="wilsoncc")
l1 <- w1[2]
u1 <- w1[3]
l2 <- w2[2]
u2 <- w2[3]
CI.lower <- max(-1, est - sqrt((p1.hat - l1)^2 + (u2-p2.hat)^2) )
CI.upper <- min( 1, est + sqrt((u1-p1.hat)^2 + (p2.hat-l2)^2) )
},
"mee" = { # Mee, also called Farrington-Mannig
.score <- function (p1, n1, p2, n2, dif) {
if(dif > 1) dif <- 1
if(dif < -1) dif <- -1
diff <- p1 - p2 - dif
if (abs(diff) == 0) {
res <- 0
} else {
t <- n2/n1
a <- 1 + t
b <- -(1 + t + p1 + t * p2 + dif * (t + 2))
c <- dif * dif + dif * (2 * p1 + t + 1) + p1 + t * p2
d <- -p1 * dif * (1 + dif)
v <- (b/a/3)^3 - b * c/(6 * a * a) + d/a/2
# v might be very small, resulting in a value v/u^3 > |1|
# causing a numeric error for acos(v/u^3)
# see: x1=10, n1=10, x2=0, n1=10
if(abs(v) < .Machine$double.eps) v <- 0
s <- sqrt((b/a/3)^2 - c/a/3)
u <- ifelse(v>0, 1,-1) * s
w <- (3.141592654 + acos(v/u^3))/3
p1d <- 2 * u * cos(w) - b/a/3
p2d <- p1d - dif
n <- n1 + n2
res <- (p1d * (1 - p1d)/n1 + p2d * (1 - p2d)/n2)
# res <- max(0, res) # might result in a value slightly negative
}
return(sqrt(res))
}
pval <- function(delta){
z <- (est - delta)/.score(p1.hat, n1, p2.hat, n2, delta)
2 * min(pnorm(z), 1-pnorm(z))
}
CI.lower <- max(-1, uniroot(
function(delta) pval(delta) - alpha,
interval = c(-1+1e-6, est-1e-6)
)$root)
CI.upper <- min(1, uniroot(
function(delta) pval(delta) - alpha,
interval = c(est + 1e-6, 1-1e-6)
)$root)
},
"blj" = { # brown-li-jeffrey
p1.dash <- (x1 + 0.5) / (n1+1)
p2.dash <- (x2 + 0.5) / (n2+1)
vd <- p1.dash*(1-p1.dash)/n1 + p2.dash*(1-p2.dash)/n2
term2 <- kappa * sqrt(vd)
est.dash <- p1.dash - p2.dash
CI.lower <- max(-1, est.dash - term2)
CI.upper <- min(1, est.dash + term2)
},
"ha" = { # Hauck-Anderson
term2 <- 1/(2*min(n1,n2)) + kappa * sqrt(p1.hat*(1-p1.hat)/(n1-1) +
p2.hat*(1-p2.hat)/(n2-1))
CI.lower <- max(-1, est - term2)
CI.upper <- min( 1, est + term2 )
},
"mn" = { # Miettinen-Nurminen
.conf <- function(x1, n1, x2, n2, z, lower=FALSE){
p1 <- x1/n1
p2 <- x2/n2
p.hat <- p1 - p2
dp <- 1 + ifelse(lower, 1, -1) * p.hat
i <- 1
while (i <= 50) {
dp <- 0.5 * dp
y <- p.hat + ifelse(lower, -1, 1) * dp
score <- .score(p1, n1, p2, n2, y)
if (score < z) {
p.hat <- y
}
if ((dp < 0.0000001) || (abs(z - score) < 0.000001))
break()
else
i <- i + 1
}
return(y)
}
.score <- function (p1, n1, p2, n2, dif) {
diff <- p1 - p2 - dif
if (abs(diff) == 0) {
res <- 0
}
else {
t <- n2/n1
a <- 1 + t
b <- -(1 + t + p1 + t * p2 + dif * (t + 2))
c <- dif * dif + dif * (2 * p1 + t + 1) + p1 + t * p2
d <- -p1 * dif * (1 + dif)
v <- (b/a/3)^3 - b * c/(6 * a * a) + d/a/2
s <- sqrt((b/a/3)^2 - c/a/3)
u <- ifelse(v>0, 1,-1) * s
w <- (3.141592654 + acos(v/u^3))/3
p1d <- 2 * u * cos(w) - b/a/3
p2d <- p1d - dif
n <- n1 + n2
var <- (p1d * (1 - p1d)/n1 + p2d * (1 - p2d)/n2) * n/(n - 1)
res <- diff^2/var
}
return(res)
}
z = qchisq(conf.level, 1)
CI.lower <- max(-1, .conf(x1, n1, x2, n2, z, TRUE))
CI.upper <- min(1, .conf(x1, n1, x2, n2, z, FALSE))
},
"beal" = {
# experimental code only...
# http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.633.9380&rep=rep1&type=pdf
a <- p1.hat + p2.hat
b <- p1.hat - p2.hat
u <- ((1/n1) + (1/n2)) / 4
v <- ((1/n1) - (1/n2)) / 4
V <- u*((2-a)*a - b^2) + 2*v*(1-a)*b
z <- qchisq(p=1-alpha/2, df = 1)
A <- sqrt(z*(V + z*u^2*(2-a)*a + z*v^2*(1-a)^2))
B <- (b + z*v*(1-a)) / (1+z*u)
CI.lower <- max(-1, B - A / (1 + z*u))
CI.upper <- min(1, B + A / (1 + z*u))
},
"hal" = { # haldane
psi <- (p1.hat + p2.hat) / 2
u <- (1/n1 + 1/n2) / 4
v <- (1/n1 - 1/n2) / 4
z <- kappa
theta <- ((p1.hat - p2.hat) + z^2 * v* (1-2*psi)) / (1+z^2*u)
w <- z / (1+z^2*u) * sqrt(u * (4*psi*(1-psi) - (p1.hat - p2.hat)^2) +
2*v*(1-2*psi) *(p1.hat - p2.hat) +
4*z^2*u^2*(1-psi)*psi + z^2*v^2*(1-2*psi)^2)
c(theta + w, theta - w)
CI.lower <- max(-1, theta - w)
CI.upper <- min(1, theta + w)
},
"jp" = { # jeffery perks
# same as haldane but with other psi
psi <- 0.5 * ((x1 + 0.5) / (n1 + 1) + (x2 + 0.5) / (n2 + 1) )
u <- (1/n1 + 1/n2) / 4
v <- (1/n1 - 1/n2) / 4
z <- kappa
theta <- ((p1.hat - p2.hat) + z^2 * v* (1-2*psi)) / (1+z^2*u)
w <- z / (1+z^2*u) * sqrt(u * (4*psi*(1-psi) - (p1.hat - p2.hat)^2) +
2*v*(1-2*psi) *(p1.hat - p2.hat) +
4*z^2*u^2*(1-psi)*psi + z^2*v^2*(1-2*psi)^2)
c(theta + w, theta - w)
CI.lower <- max(-1, theta - w)
CI.upper <- min(1, theta + w)
},
)
ci <- c(est = est, lwr.ci = min(CI.lower, CI.upper), upr.ci = max(CI.lower, CI.upper))
if(sides=="left")
ci[3] <- 1
else if(sides=="right")
ci[2] <- -1
return(ci)
}
method <- match.arg(arg=method, several.ok = TRUE)
sides <- match.arg(arg=sides, several.ok = TRUE)
# Recycle arguments
lst <- Recycle(x1=x1, n1=n1, x2=x2, n2=n2, conf.level=conf.level, sides=sides, method=method)
res <- t(sapply(1:attr(lst, "maxdim"),
function(i) iBinomDiffCI(x1=lst$x1[i], n1=lst$n1[i], x2=lst$x2[i], n2=lst$n2[i],
conf.level=lst$conf.level[i],
sides=lst$sides[i],
method=lst$method[i])))
# get rownames
lgn <- Recycle(x1=if(is.null(names(x1))) paste("x1", seq_along(x1), sep=".") else names(x1),
n1=if(is.null(names(n1))) paste("n1", seq_along(n1), sep=".") else names(n1),
x2=if(is.null(names(x2))) paste("x2", seq_along(x2), sep=".") else names(x2),
n2=if(is.null(names(n2))) paste("n2", seq_along(n2), sep=".") else names(n2),
conf.level=conf.level, sides=sides, method=method)
xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) length(unique(x)) != 1)]), 1, paste, collapse=":")
rownames(res) <- xn
return(res)
}
BinomRatioCI_old <- function(x1, n1, x2, n2, conf.level = 0.95, method = "katz.log", bonf = FALSE,
tol = .Machine$double.eps^0.25, R = 1000, r = length(x1)) {
# source: asbio::ci.prat by Ken Aho <kenaho1 at gmail.com>
conf <- conf.level
x <- x1; m <- n1; y <- x2; n <- n2
indices <- c("adj.log","bailey","boot","katz.log","koopman","noether","sinh-1")
method <- match.arg(method, indices)
if(any(c(length(m),length(y),length(n))!= length(x))) stop("x1, n1, x2, and n2 vectors must have equal length")
alpha <- 1 - conf
oconf <- conf
conf <- ifelse(bonf == FALSE, conf, 1 - alpha/r)
z.star <- qnorm(1 - (1 - conf)/2)
x2 <- qchisq(conf, 1)
ci.prat1 <- function(x, m, y, n, conf = 0.95, method = "katz.log", bonf = FALSE){
if((x > m)|(y > n)) stop("Use correct parameterization for x1, x2, n1, and n2")
#-------------------------- Adj-log ------------------------------#
if(method == "adj.log"){
if((x == m & y == n)){
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- ((x+0.5)/(m+0.5))/((y+0.5)/(n+0.5)); varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
CIU <- nrat * exp(z.star * sqrt(varhat))
} else if(x == 0 & y == 0){CIL = 0; CIU = Inf; rat = 0; varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
}else{
rat <- (x/m)/(y/n); nrat <- ((x+0.5)/(m+0.5))/((y+0.5)/(n+0.5)); varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
CIU <- nrat * exp(z.star * sqrt(varhat))}
CI <- c(rat, CIL, CIU)
}
#-------------------------------Bailey-----------------------------#
if(method == "bailey"){
rat <- (x/m)/(y/n)
varhat <- ifelse((x == m) & (y == n),(1/(m-0.5)) - (1/(m)) + (1/(n-0.5)) - (1/(n)),(1/(x)) - (1/(m)) + (1/(y)) - (1/(n)))
p.hat1 <- x/m; p.hat2 <- y/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
if(x == 0 | y == 0){
xn <- ifelse(x == 0, 0.5, x)
yn <- ifelse(y == 0, 0.5, y)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
if(xn == m | yn == n){
xn <- ifelse(xn == m, m - 0.5, xn)
yn <- ifelse(yn == n, n - 0.5, yn)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
}
}
if(x == 0 | y == 0){
if(x == 0 & y == 0){
rat <- Inf
CIL <- 0
CIU <- Inf
}
if(x == 0 & y != 0){
CIL <- 0
CIU <- nrat * ((1+ z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}
if(y == 0 & x != 0){
CIU = Inf
CIL <- nrat * ((1- z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}
}else if(x == m | y == n){
xn <- ifelse(x == m, m - 0.5, x)
yn <- ifelse(y == n, n - 0.5, y)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
CIL <- nrat * ((1- z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
CIU <- nrat * ((1+ z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}else{
CIL <- rat * ((1- z.star * sqrt((q.hat1/x) + (q.hat2/y) - (z.star^2 * q.hat1 * q.hat2)/(9 * x * y))/3)/((1 - (z.star^2 * q.hat2)/(9 * y))))^3
CIU <- rat * ((1+ z.star * sqrt((q.hat1/x) + (q.hat2/y) - (z.star^2 * q.hat1 * q.hat2)/(9 * x * y))/3)/((1 - (z.star^2 * q.hat2)/(9 * y))))^3
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Boot ------------------------------#
if(method == "boot"){
rat <- (x/m)/(y/n)
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {CIL <- 0; rat <- (x/m)/(y/n); x <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIU <- nrat * exp(z.star * sqrt(varhat))}
if(x != 0 & y == 0) {CIU <- Inf; rat <- (x/m)/(y/n); y <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))}
} else{
num.data <- c(rep(1, x), rep(0, m - x))
den.data <- c(rep(1, y), rep(0, n - y))
nd <- matrix(ncol = R, nrow = m)
dd <- matrix(ncol = R, nrow = n)
brat <- 1:R
for(i in 1L:R){
nd[,i] <- sample(num.data, m, replace = TRUE)
dd[,i] <- sample(den.data, n, replace = TRUE)
brat[i] <- (sum(nd[,i])/m)/(sum(dd[,i])/n)
}
alpha <- 1 - conf
CIU <- quantile(brat, 1 - alpha/2, na.rm = TRUE)
CIL <- quantile(brat, alpha/2, na.rm = TRUE)
varhat <- var(brat)
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Katz-log ------------------------------#
if(method == "katz.log"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {CIL <- 0; rat <- (x/m)/(y/n); x <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIU <- nrat * exp(z.star * sqrt(varhat))}
if(x != 0 & y == 0) {CIU <- Inf; rat <- (x/m)/(y/n); y <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n); CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n); CIU <- nrat * exp(z.star * sqrt(varhat))
}
} else
{rat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- rat * exp(-1 * z.star * sqrt(varhat))
CIU <- rat * exp(z.star * sqrt(varhat))}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Koopman ------------------------------#
if(method == "koopman"){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA
} else {
a1 = n * (n * (n + m) * x + m * (n + x) * (z.star^2))
a2 = -n * (n * m * (y + x) + 2 * (n + m) * y *
x + m * (n + y + 2 * x) * (z.star^2))
a3 = 2 * n * m * y * (y + x) + (n + m) * (y^2) *
x + n * m * (y + x) * (z.star^2)
a4 = -m * (y^2) * (y + x)
b1 = a2/a1; b2 = a3/a1; b3 = a4/a1
c1 = b2 - (b1^2)/3; c2 = b3 - b1 * b2/3 + 2 * (b1^3)/27
ceta = suppressWarnings(acos(sqrt(27) * c2/(2 * c1 * sqrt(-c1))))
t1 <- suppressWarnings(-2 * sqrt(-c1/3) * cos(pi/3 - ceta/3))
t2 <- suppressWarnings(-2 * sqrt(-c1/3) * cos(pi/3 + ceta/3))
t3 <- suppressWarnings(2 * sqrt(-c1/3) * cos(ceta/3))
p01 = t1 - b1/3; p02 = t2 - b1/3; p03 = t3 - b1/3
p0sum = p01 + p02 + p03; p0up = min(p01, p02, p03); p0low = p0sum - p0up - max(p01, p02, p03)
U <- function(a){
p.hatf <- function(a){
(a * (m + y) + x + n - ((a * (m + y) + x + n)^2 - 4 * a * (m + n) * (x + y))^0.5)/(2 * (m + n))
}
p.hat <- p.hatf(a)
(((x - m * p.hat)^2)/(m * p.hat * (1 - p.hat)))*(1 + (m * (a - p.hat))/(n * (1 - p.hat))) - x2
}
rat <- (x/m)/(y/n); nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n)
if((x == 0) & (y != 0)) {nrat <- ((x + 0.5)/m)/(y/n); varhat <- (1/(x + 0.5)) - (1/m) + (1/y) - (1/n)}
if((y == 0) & (x != 0)) {nrat <- (x/m)/((y + 0.5)/n); varhat <- (1/x) - (1/m) + (1/(y + 0.5)) - (1/n)}
if((y == n) & (x == m)) {nrat <- 1; varhat <- (1/(m - 0.5)) - (1/m) + 1/(n - 0.5) - (1/n)}
La <- nrat * exp(-1 * z.star * sqrt(varhat)) * 1/4
Lb <- nrat
Ha <- nrat
Hb <- nrat * exp(z.star * sqrt(varhat)) * 4
#----------------------------------------------------------------------------#
if((x != 0) & (y == 0)) {
if(x == m){
CIL = (1 - (m - x) * (1 - p0low)/(y + m - (n + m) * p0low))/p0low
CIU <- Inf
}
else{
CIL <- uniroot(U, c(La, Lb), tol=tol)$root
CIU <- Inf
}
}
#------------------------------------------------------------#
if((x == 0) & (y != n)) {
CIU <- uniroot(U, c(Ha, Hb), tol=tol)$root
CIL <- 0
}
#------------------------------------------------------------#
if(((x == m)|(y == n)) & (y != 0)){
if((x == m)&(y == n)){
U.0 <- function(a){if(a <= 1) {m * (1 - a)/a - x2}
else{(n * (a - 1)) - x2}
}
CIL <- uniroot(U.0, c(La, rat), tol = tol)$root
CIU <- uniroot(U.0, c(rat, Hb), tol = tol)$root
}
#------------------------------------------------------------#
if((x == m) & (y != n)){
phat1 = x/m; phat2 = y/n
phihat = phat2/phat1
phiu = 1.1 * phihat
r = 0
while (r >= -z.star) {
a = (m + n) * phiu
b = -((x + n) * phiu + y + m)
c = x + y
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phiu
q2hat = 1 - p2hat
var = (m * n * p2hat)/(n * (phiu - p2hat) +
m * q2hat)
r = ((y - n * p2hat)/q2hat)/sqrt(var)
phiu1 = phiu
phiu = 1.0001 * phiu1
}
CIU = (1 - (m - x) * (1 - p0up)/(y + m - (n + m) * p0up))/p0up
CIL = 1/phiu1
}
#------------------------------------------------------------#
if((y == n) & (x != m)){
p.hat2 = y/n; p.hat1 = x/m; phihat = p.hat1/p.hat2
phil = 0.95 * phihat; r = 0
if(x != 0){
while(r <= z.star) {
a = (n + m) * phil
b = -((y + m) * phil + x + n)
c = y + x
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phil
q2hat = 1 - p2hat
var = (n * m * p2hat)/(m * (phil - p2hat) +
n * q2hat)
r = ((x - m * p2hat)/q2hat)/sqrt(var)
CIL = phil
phil = CIL/1.0001
}
}
phiu = 1.1 * phihat
if(x == 0){CIL = 0; phiu <- ifelse(n < 100, 0.01, 0.001)}
r = 0
while(r >= -z.star) {
a = (n + m) * phiu
b = -((y + m) * phiu + x + n)
c = y + x
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phiu
q2hat = 1 - p2hat
var = (n * m * p2hat)/(m * (phiu - p2hat) +
n * q2hat)
r = ((x - m * p2hat)/q2hat)/sqrt(var)
phiu1 = phiu
phiu = 1.0001 * phiu1
}
CIU <- phiu1
}
} else if((y != n) & (x != m) & (x != 0) & (y != 0)){
CIL <- uniroot(U, c(La, Lb), tol=tol)$root
CIU <- uniroot(U, c(Ha, Hb), tol=tol)$root
}
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Noether ------------------------------#
if(method == "noether"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; se.hat <- NA; varhat = NA}
if(x == 0 & y != 0) {rat <- (x/m)/(y/n); CIL <- 0; x <- 0.5
nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIU <- nrat + z.star * se.hat}
if(x != 0 & y == 0) {rat <- Inf; CIU <- Inf; y <- 0.5
nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIL <- nrat - z.star * se.hat}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIU <- nrat + z.star * se.hat
CIL <- nrat - z.star * se.hat
}
} else
{
rat <- (x/m)/(y/n)
se.hat <- rat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIL <- rat - z.star * se.hat
CIU <- rat + z.star * se.hat
}
varhat <- ifelse(is.na(se.hat), NA, se.hat^2)
CI <- c(rat, max(0,CIL), CIU)
}
#------------------------- sinh-1 -----------------------------#
if(method == "sinh-1"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {rat <- (x/m)/(y/n); CIL <- 0; x <- z.star
nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIU <- exp(log(nrat) + varhat)}
if(x != 0 & y == 0) {rat = Inf; CIU <- Inf; y <- z.star
nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(nrat) - varhat)}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(nrat) - varhat)
CIU <- exp(log(nrat) + varhat)
}
} else
{rat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(rat) - varhat)
CIU <- exp(log(rat) + varhat)
}
CI <- c(rat, CIL, CIU)
}
#------------------------Results ------------------------------#
res <- list(CI = CI, varhat = varhat)
res
}
CI <- matrix(ncol = 3, nrow = length(x1))
vh <- rep(NA, length(x1))
for(i in 1L : length(x1)){
temp <- ci.prat1(x = x[i], m = m[i], y = y[i], n = n[i], conf = conf, method = method, bonf = bonf)
CI[i,] <- temp$CI
vh[i] <- temp$varhat
}
CI <- data.frame(CI)
if(length(x1) == 1) row.names(CI) <- ""
head <- paste(paste(as.character(oconf * 100),"%",sep=""), c("Confidence interval for ratio of binomial proportions"))
if(method == "adj.log")head <- paste(head,"(method=adj-log)")
if(method == "bailey")head <- paste(head,"(method=Bailey)")
if(method == "boot")head <- paste(head,"(method=percentile bootstrap)")
if(method == "katz.log")head <- paste(head,"(method=Katz-log)")
if(method == "koopman")head <- paste(head,"(method=Koopman)")
if(method == "noether")head <- paste(head,"(method=Noether)")
if(method == "sinh")head <- paste(head,"(method=sinh^-1)")
if(bonf == TRUE)head <- paste(head, "\n Bonferroni simultaneous intervals, r = ", bquote(.(r)),
"\n Marginal confidence = ", bquote(.(conf)), "\n", sep = "")
ends <- c("Estimate", paste(as.character(c((1-oconf)/2, 1-((1-oconf)/2))*100), "%", sep=""))
# res <- list(varhat = vh, ci = CI, ends = ends, head = head)
# class(res) <- "ci"
res <- data.matrix(CI)
dimnames(res) <- list(NULL, c("est","lwr.ci","upr.ci"))
res
}
BinomRatioCI <- function(x1, n1, x2, n2, conf.level = 0.95, sides = c("two.sided","left","right"),
method =c("katz.log","adj.log","bailey","koopman","noether","sinh-1","boot"),
tol = .Machine$double.eps^0.25, R = 1000) {
# source: asbio::ci.prat by Ken Aho <kenaho1 at gmail.com>
iBinomRatioCI <- function(x, m, y, n, conf, sides, method) {
if((x > m)|(y > n)) stop("Use correct parameterization for x1, x2, n1, and n2")
method <- match.arg(method, c("katz.log","adj.log","bailey","koopman","noether","sinh-1","boot"))
if(sides!="two.sided")
conf <- 1 - 2*(1-conf)
alpha <- 1 - conf
z.star <- qnorm(1 - (1 - conf)/2)
x2 <- qchisq(conf, 1)
#-------------------------- Adj-log ------------------------------#
if(method == "adj.log"){
if((x == m & y == n)){
rat <- (x/m)/(y/n)
x <- m - 0.5
y <- n - 0.5
nrat <- ((x+0.5)/(m+0.5))/((y+0.5)/(n+0.5))
varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
CIU <- nrat * exp(z.star * sqrt(varhat))
} else if(x == 0 & y == 0){
CIL = 0
CIU = Inf
rat = 0
varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
} else {
rat <- (x/m)/(y/n)
nrat <- ((x+0.5)/(m+0.5))/((y+0.5)/(n+0.5))
varhat <- (1/(x+0.5)) - (1/(m+0.5)) + (1/(y+0.5)) - (1/(n+0.5))
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
CIU <- nrat * exp(z.star * sqrt(varhat))
}
CI <- c(rat, CIL, CIU)
}
#-------------------------------Bailey-----------------------------#
if(method == "bailey"){
rat <- (x/m)/(y/n)
varhat <- ifelse((x == m) & (y == n),(1/(m-0.5)) - (1/(m)) + (1/(n-0.5)) - (1/(n)),(1/(x)) - (1/(m)) + (1/(y)) - (1/(n)))
p.hat1 <- x/m; p.hat2 <- y/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
if(x == 0 | y == 0){
xn <- ifelse(x == 0, 0.5, x)
yn <- ifelse(y == 0, 0.5, y)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
if(xn == m | yn == n){
xn <- ifelse(xn == m, m - 0.5, xn)
yn <- ifelse(yn == n, n - 0.5, yn)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
}
}
if(x == 0 | y == 0){
if(x == 0 & y == 0){
rat <- Inf
CIL <- 0
CIU <- Inf
}
if(x == 0 & y != 0){
CIL <- 0
CIU <- nrat * ((1+ z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}
if(y == 0 & x != 0){
CIU = Inf
CIL <- nrat * ((1- z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}
}else if(x == m | y == n){
xn <- ifelse(x == m, m - 0.5, x)
yn <- ifelse(y == n, n - 0.5, y)
nrat <- (xn/m)/(yn/n)
p.hat1 <- xn/m; p.hat2 <- yn/n;
q.hat1 <- 1 - p.hat1; q.hat2 <- 1 - p.hat2
CIL <- nrat * ((1- z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
CIU <- nrat * ((1+ z.star * sqrt((q.hat1/xn) + (q.hat2/yn) - (z.star^2 * q.hat1 * q.hat2)/(9 * xn * yn))/3)/((1 - (z.star^2 * q.hat2)/(9 * yn))))^3
}else{
CIL <- rat * ((1- z.star * sqrt((q.hat1/x) + (q.hat2/y) - (z.star^2 * q.hat1 * q.hat2)/(9 * x * y))/3)/((1 - (z.star^2 * q.hat2)/(9 * y))))^3
CIU <- rat * ((1+ z.star * sqrt((q.hat1/x) + (q.hat2/y) - (z.star^2 * q.hat1 * q.hat2)/(9 * x * y))/3)/((1 - (z.star^2 * q.hat2)/(9 * y))))^3
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Boot ------------------------------#
if(method == "boot"){
rat <- (x/m)/(y/n)
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {CIL <- 0; rat <- (x/m)/(y/n); x <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIU <- nrat * exp(z.star * sqrt(varhat))}
if(x != 0 & y == 0) {CIU <- Inf; rat <- (x/m)/(y/n); y <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))}
} else{
num.data <- c(rep(1, x), rep(0, m - x))
den.data <- c(rep(1, y), rep(0, n - y))
nd <- matrix(ncol = R, nrow = m)
dd <- matrix(ncol = R, nrow = n)
brat <- 1:R
for(i in 1L:R){
nd[,i] <- sample(num.data, m, replace = TRUE)
dd[,i] <- sample(den.data, n, replace = TRUE)
brat[i] <- (sum(nd[,i])/m)/(sum(dd[,i])/n)
}
alpha <- 1 - conf
CIU <- quantile(brat, 1 - alpha/2, na.rm = TRUE)
CIL <- quantile(brat, alpha/2, na.rm = TRUE)
varhat <- var(brat)
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Katz-log ------------------------------#
if(method == "katz.log"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {CIL <- 0; rat <- (x/m)/(y/n); x <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIU <- nrat * exp(z.star * sqrt(varhat))}
if(x != 0 & y == 0) {CIU <- Inf; rat <- (x/m)/(y/n); y <- 0.5; nrat <- (x/m)/(y/n)
varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- nrat * exp(-1 * z.star * sqrt(varhat))}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n); CIL <- nrat * exp(-1 * z.star * sqrt(varhat))
x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n); CIU <- nrat * exp(z.star * sqrt(varhat))
}
} else
{rat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n)
CIL <- rat * exp(-1 * z.star * sqrt(varhat))
CIU <- rat * exp(z.star * sqrt(varhat))}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Koopman ------------------------------#
if(method == "koopman"){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA
} else {
a1 = n * (n * (n + m) * x + m * (n + x) * (z.star^2))
a2 = -n * (n * m * (y + x) + 2 * (n + m) * y *
x + m * (n + y + 2 * x) * (z.star^2))
a3 = 2 * n * m * y * (y + x) + (n + m) * (y^2) *
x + n * m * (y + x) * (z.star^2)
a4 = -m * (y^2) * (y + x)
b1 = a2/a1; b2 = a3/a1; b3 = a4/a1
c1 = b2 - (b1^2)/3; c2 = b3 - b1 * b2/3 + 2 * (b1^3)/27
ceta = suppressWarnings(acos(sqrt(27) * c2/(2 * c1 * sqrt(-c1))))
t1 <- suppressWarnings(-2 * sqrt(-c1/3) * cos(pi/3 - ceta/3))
t2 <- suppressWarnings(-2 * sqrt(-c1/3) * cos(pi/3 + ceta/3))
t3 <- suppressWarnings(2 * sqrt(-c1/3) * cos(ceta/3))
p01 = t1 - b1/3; p02 = t2 - b1/3; p03 = t3 - b1/3
p0sum = p01 + p02 + p03; p0up = min(p01, p02, p03); p0low = p0sum - p0up - max(p01, p02, p03)
U <- function(a){
p.hatf <- function(a){
(a * (m + y) + x + n - ((a * (m + y) + x + n)^2 - 4 * a * (m + n) * (x + y))^0.5)/(2 * (m + n))
}
p.hat <- p.hatf(a)
(((x - m * p.hat)^2)/(m * p.hat * (1 - p.hat)))*(1 + (m * (a - p.hat))/(n * (1 - p.hat))) - x2
}
rat <- (x/m)/(y/n); nrat <- (x/m)/(y/n); varhat <- (1/x) - (1/m) + (1/y) - (1/n)
if((x == 0) & (y != 0)) {nrat <- ((x + 0.5)/m)/(y/n); varhat <- (1/(x + 0.5)) - (1/m) + (1/y) - (1/n)}
if((y == 0) & (x != 0)) {nrat <- (x/m)/((y + 0.5)/n); varhat <- (1/x) - (1/m) + (1/(y + 0.5)) - (1/n)}
if((y == n) & (x == m)) {nrat <- 1; varhat <- (1/(m - 0.5)) - (1/m) + 1/(n - 0.5) - (1/n)}
La <- nrat * exp(-1 * z.star * sqrt(varhat)) * 1/4
Lb <- nrat
Ha <- nrat
Hb <- nrat * exp(z.star * sqrt(varhat)) * 4
#----------------------------------------------------------------------------#
if((x != 0) & (y == 0)) {
if(x == m){
CIL = (1 - (m - x) * (1 - p0low)/(y + m - (n + m) * p0low))/p0low
CIU <- Inf
}
else{
CIL <- uniroot(U, c(La, Lb), tol=tol)$root
CIU <- Inf
}
}
#------------------------------------------------------------#
if((x == 0) & (y != n)) {
CIU <- uniroot(U, c(Ha, Hb), tol=tol)$root
CIL <- 0
}
#------------------------------------------------------------#
if(((x == m)|(y == n)) & (y != 0)){
if((x == m)&(y == n)){
U.0 <- function(a){if(a <= 1) {m * (1 - a)/a - x2}
else{(n * (a - 1)) - x2}
}
CIL <- uniroot(U.0, c(La, rat), tol = tol)$root
CIU <- uniroot(U.0, c(rat, Hb), tol = tol)$root
}
#------------------------------------------------------------#
if((x == m) & (y != n)){
phat1 = x/m; phat2 = y/n
phihat = phat2/phat1
phiu = 1.1 * phihat
r = 0
while (r >= -z.star) {
a = (m + n) * phiu
b = -((x + n) * phiu + y + m)
c = x + y
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phiu
q2hat = 1 - p2hat
var = (m * n * p2hat)/(n * (phiu - p2hat) +
m * q2hat)
r = ((y - n * p2hat)/q2hat)/sqrt(var)
phiu1 = phiu
phiu = 1.0001 * phiu1
}
CIU = (1 - (m - x) * (1 - p0up)/(y + m - (n + m) * p0up))/p0up
CIL = 1/phiu1
}
#------------------------------------------------------------#
if((y == n) & (x != m)){
p.hat2 = y/n; p.hat1 = x/m; phihat = p.hat1/p.hat2
phil = 0.95 * phihat; r = 0
if(x != 0){
while(r <= z.star) {
a = (n + m) * phil
b = -((y + m) * phil + x + n)
c = y + x
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phil
q2hat = 1 - p2hat
var = (n * m * p2hat)/(m * (phil - p2hat) +
n * q2hat)
r = ((x - m * p2hat)/q2hat)/sqrt(var)
CIL = phil
phil = CIL/1.0001
}
}
phiu = 1.1 * phihat
if(x == 0){CIL = 0; phiu <- ifelse(n < 100, 0.01, 0.001)}
r = 0
while(r >= -z.star) {
a = (n + m) * phiu
b = -((y + m) * phiu + x + n)
c = y + x
p1hat = (-b - sqrt(b^2 - 4 * a * c))/(2 * a)
p2hat = p1hat * phiu
q2hat = 1 - p2hat
var = (n * m * p2hat)/(m * (phiu - p2hat) +
n * q2hat)
r = ((x - m * p2hat)/q2hat)/sqrt(var)
phiu1 = phiu
phiu = 1.0001 * phiu1
}
CIU <- phiu1
}
} else if((y != n) & (x != m) & (x != 0) & (y != 0)){
CIL <- uniroot(U, c(La, Lb), tol=tol)$root
CIU <- uniroot(U, c(Ha, Hb), tol=tol)$root
}
}
CI <- c(rat, CIL, CIU)
}
#-------------------------- Noether ------------------------------#
if(method == "noether"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; se.hat <- NA; varhat = NA}
if(x == 0 & y != 0) {rat <- (x/m)/(y/n); CIL <- 0; x <- 0.5
nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIU <- nrat + z.star * se.hat}
if(x != 0 & y == 0) {rat <- Inf; CIU <- Inf; y <- 0.5
nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIL <- nrat - z.star * se.hat}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); se.hat <- nrat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIU <- nrat + z.star * se.hat
CIL <- nrat - z.star * se.hat
}
} else
{
rat <- (x/m)/(y/n)
se.hat <- rat * sqrt((1/x) - (1/m) + (1/y) - (1/n))
CIL <- rat - z.star * se.hat
CIU <- rat + z.star * se.hat
}
varhat <- ifelse(is.na(se.hat), NA, se.hat^2)
CI <- c(rat, max(0,CIL), CIU)
}
#------------------------- sinh-1 -----------------------------#
if(method == "sinh-1"){
if((x == 0 & y == 0)|(x == 0 & y != 0)|(x != 0 & y == 0)|(x == m & y == n)){
if(x == 0 & y == 0) {CIL <- 0; CIU <- Inf; rat = 0; varhat = NA}
if(x == 0 & y != 0) {rat <- (x/m)/(y/n); CIL <- 0; x <- z.star
nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIU <- exp(log(nrat) + varhat)}
if(x != 0 & y == 0) {rat = Inf; CIU <- Inf; y <- z.star
nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(nrat) - varhat)}
if(x == m & y == n) {
rat <- (x/m)/(y/n); x <- m - 0.5; y <- n - 0.5; nrat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(nrat) - varhat)
CIU <- exp(log(nrat) + varhat)
}
} else
{rat <- (x/m)/(y/n); varhat <- 2 * asinh((z.star/2)*sqrt(1/x + 1/y - 1/m - 1/n))
CIL <- exp(log(rat) - varhat)
CIU <- exp(log(rat) + varhat)
}
CI <- c(rat, CIL, CIU)
}
#------------------------Results ------------------------------#
# res <- list(CI = CI, varhat = varhat)
CI
}
if(missing(sides)) sides <- match.arg(sides)
if(missing(method)) method <- match.arg(method)
# Recycle arguments
lst <- Recycle(x1=x1, n1=n1, x2=x2, n2=n2, conf.level=conf.level, sides=sides, method=method)
# iBinomRatioCI <- function(x, m, y, n, conf, method){
res <- t(sapply(1:attr(lst, "maxdim"),
function(i) iBinomRatioCI(x=lst$x1[i], m=lst$n1[i], y=lst$x2[i], n=lst$n2[i],
conf=lst$conf.level[i],
sides=lst$sides[i],
method=lst$method[i])))
# get rownames
lgn <- Recycle(x1=if(is.null(names(x1))) paste("x1", seq_along(x1), sep=".") else names(x1),
n1=if(is.null(names(n1))) paste("n1", seq_along(n1), sep=".") else names(n1),
x2=if(is.null(names(x2))) paste("x2", seq_along(x2), sep=".") else names(x2),
n2=if(is.null(names(n2))) paste("n2", seq_along(n2), sep=".") else names(n2),
conf.level=conf.level, sides=sides, method=method)
xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) length(unique(x)) != 1)]), 1, paste, collapse=":")
return(SetNames(res,
rownames=xn,
colnames=c("est", "lwr.ci", "upr.ci")))
}
MultinomCI <- function(x, conf.level = 0.95, sides = c("two.sided","left","right"),
method = c("sisonglaz", "cplus1", "goodman", "wald", "waldcc", "wilson", "qh", "fs")) {
# Code mainly by:
# Pablo J. Villacorta Iglesias <pjvi@decsai.ugr.es>\n
# Department of Computer Science and Artificial Intelligence, University of Granada (Spain)
.moments <- function(c, lambda){
a <- lambda + c
b <- lambda - c
if(b < 0) b <- 0
if(b > 0) den <- ppois(a, lambda) - ppois(b-1, lambda)
if(b == 0) den <- ppois(a,lambda)
mu <- mat.or.vec(4,1)
mom <- mat.or.vec(5,1)
for(r in 1:4){
poisA <- 0
poisB <- 0
if((a-r) >=0){ poisA <- ppois(a,lambda)-ppois(a-r,lambda) }
if((a-r) < 0){ poisA <- ppois(a,lambda) }
if((b-r-1) >=0){ poisB <- ppois(b-1,lambda)-ppois(b-r-1,lambda) }
if((b-r-1) < 0 && (b-1)>=0){ poisB <- ppois(b-1,lambda) }
if((b-r-1) < 0 && (b-1) < 0){ poisB <- 0 }
mu[r] <- (lambda^r)*(1-(poisA-poisB)/den)
}
mom[1] <- mu[1]
mom[2] <- mu[2] + mu[1] - mu[1]^2
mom[3] <- mu[3] + mu[2]*(3-3*mu[1]) + (mu[1]-3*mu[1]^2+2*mu[1]^3)
mom[4] <- mu[4] + mu[3]*(6-4*mu[1]) + mu[2]*(7-12*mu[1]+6*mu[1]^2)+mu[1]-4*mu[1]^2+6*mu[1]^3-3*mu[1]^4
mom[5] <- den
return(mom)
}
.truncpoi <- function(c, x, n, k){
m <- matrix(0, k, 5)
for(i in 1L:k){
lambda <- x[i]
mom <- .moments(c, lambda)
for(j in 1L:5L){ m[i,j] <- mom[j] }
}
for(i in 1L:k){ m[i, 4] <- m[i, 4] - 3 * m[i, 2]^2 }
s <- colSums(m)
s1 <- s[1]
s2 <- s[2]
s3 <- s[3]
s4 <- s[4]
probn <- 1/(ppois(n,n)-ppois(n-1,n))
z <- (n-s1)/sqrt(s2)
g1 <- s3/(s2^(3/2))
g2 <- s4/(s2^2)
poly <- 1 + g1*(z^3-3*z)/6 + g2*(z^4-6*z^2+3)/24
+ g1^2*(z^6-15*z^4 + 45*z^2-15)/72
f <- poly*exp(-z^2/2)/(sqrt(2)*gamma(0.5))
probx <- 1
for(i in 1L:k){ probx <- probx * m[i,5] }
return(probn * probx * f / sqrt(s2))
}
n <- sum(x, na.rm=TRUE)
k <- length(x)
p <- x/n
if (missing(method)) method <- "sisonglaz"
if(missing(sides)) sides <- "two.sided"
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
method <- match.arg(arg = method, choices = c("sisonglaz", "cplus1", "goodman", "wald", "waldcc", "wilson", "qh", "fs"))
if(method == "goodman") {
# erroneous: q.chi <- qchisq(conf.level, k - 1)
# corrected on
q.chi <- qchisq(1 - (1-conf.level)/k, df = 1)
lci <- (q.chi + 2*x - sqrt(q.chi*(q.chi + 4*x*(n-x)/n))) / (2*(n+q.chi))
uci <- (q.chi + 2*x + sqrt(q.chi*(q.chi + 4*x*(n-x)/n))) / (2*(n+q.chi))
res <- cbind(est=p, lwr.ci=pmax(0, lci), upr.ci=pmin(1, uci))
} else if(method == "wald") {
q.chi <- qchisq(conf.level, 1)
lci <- p - sqrt(q.chi * p * (1 - p)/n)
uci <- p + sqrt(q.chi * p * (1 - p)/n)
res <- cbind(est=p, lwr.ci=pmax(0, lci), upr.ci=pmin(1, uci))
} else if(method == "waldcc") {
q.chi <- qchisq(conf.level, 1)
lci <- p - sqrt(q.chi * p * (1 - p)/n) - 1/(2*n)
uci <- p + sqrt(q.chi * p * (1 - p)/n) + 1/(2*n)
res <- cbind(est=p, lwr.ci=pmax(0, lci), upr.ci=pmin(1, uci))
} else if(method == "wilson") {
q.chi <- qchisq(conf.level, 1)
lci <- (q.chi + 2*x - sqrt(q.chi^2 + 4*x*q.chi * (1 - p))) / (2*(q.chi + n))
uci <- (q.chi + 2*x + sqrt(q.chi^2 + 4*x*q.chi * (1 - p))) / (2*(q.chi + n))
res <- cbind(est=p, lwr.ci=pmax(0, lci), upr.ci=pmin(1, uci))
} else if (method == "fs") {
# references Fitzpatrick, S. and Scott, A. (1987). Quick simultaneous confidence
# interval for multinomial proportions.
# Journal of American Statistical Association 82(399): 875-878.
q.snorm <- qnorm(1-(1 - conf.level)/2)
lci <- p - q.snorm / (2 * sqrt(n))
uci <- p + q.snorm / (2 * sqrt(n))
res <- cbind(est = p, lwr.ci = pmax(0, lci), upr.ci = pmin(1, uci))
} else if (method == "qh") {
# references Quesensberry, C.P. and Hurst, D.C. (1964).
# Large Sample Simultaneous Confidence Intervals for
# Multinational Proportions. Technometrics, 6: 191-195.
q.chi <- qchisq(conf.level, df = k-1)
lci <- (q.chi + 2*x - sqrt(q.chi^2 + 4*x*q.chi*(1 - p)))/(2*(q.chi+n))
uci <- (q.chi + 2*x + sqrt(q.chi^2 + 4*x*q.chi*(1 - p)))/(2*(q.chi+n))
res <- cbind(est = p, lwr.ci = pmax(0, lci), upr.ci = pmin(1, uci))
} else { # sisonglaz, cplus1
const <- 0
pold <- 0
for(cc in 1:n){
poi <- .truncpoi(cc, x, n, k)
if(poi > conf.level && pold < conf.level) {
const <- cc
break
}
pold <- poi
}
delta <- (conf.level - pold)/(poi - pold)
const <- const - 1
if(method == "sisonglaz") {
res <- cbind(est = p, lwr.ci = pmax(0, p - const/n), upr.ci = pmin(1, p + const/n + 2*delta/n))
} else if(method == "cplus1") {
res <- cbind(est = p, lwr.ci = pmax(0, p - const/n - 1/n), upr.ci = pmin(1,p + const/n + 1/n))
}
}
if(sides=="left")
res[3] <- 1
else if(sides=="right")
res[2] <- 0
return(res)
}
# Confidence Intervals for Poisson mean
PoissonCI <- function(x, n = 1, conf.level = 0.95, sides = c("two.sided","left","right"),
method = c("exact","score", "wald","byar")) {
if(missing(method)) method <- "exact"
if(missing(sides)) sides <- "two.sided"
iPoissonCI <- function(x, n = 1, conf.level = 0.95, sides = c("two.sided","left","right"),
method = c("exact","score", "wald","byar")) {
# ref: http://www.ijmo.org/papers/189-S083.pdf but wacklig!!!
# http://www.math.montana.edu/~rjboik/classes/502/ci.pdf
# http://www.ine.pt/revstat/pdf/rs120203.pdf
# http://www.pvamu.edu/include/Math/AAM/AAM%20Vol%206,%20Issue%201%20(June%202011)/06_%20Kibria_AAM_R308_BK_090110_Vol_6_Issue_1.pdf
# see also: pois.conf.int {epitools}
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
if(missing(method)) method <- "score"
if(length(conf.level) != 1) stop("'conf.level' has to be of length 1 (confidence level)")
if(conf.level < 0.5 | conf.level > 1) stop("'conf.level' has to be in [0.5, 1]")
alpha <- 1 - conf.level
z <- qnorm(1-alpha/2)
lambda <- x/n
switch( match.arg(arg=method, choices=c("exact","score", "wald","byar"))
, "exact" = {
ci <- poisson.test(x, n, conf.level = conf.level)$conf.int
lwr.ci <- ci[1]
upr.ci <- ci[2]
}
, "score" = {
term1 <- (x + z^2/2)/n
term2 <- z * n^-0.5 * sqrt(x/n + z^2/(4*n))
lwr.ci <- term1 - term2
upr.ci <- term1 + term2
}
, "wald" = {
term2 <- z*sqrt(lambda/n)
lwr.ci <- lambda - term2
upr.ci <- lambda + term2
}
, "byar" = {
xcc <- x + 0.5
zz <- (z/3) * sqrt(1/xcc)
lwr.ci <- (xcc * (1 - 1/(9 * xcc) - zz)^3)/n
upr.ci <- (xcc * (1 - 1/(9 * xcc) + zz)^3)/n
}
# agresti-coull is the same as score
# , "agresti-coull" = {
# lwr.ci <- lambda + z^2/(2*n) - z*sqrt(lambda/n + z^2/(4*n^2))
# upr.ci <- lambda + z^2/(2*n) + z*sqrt(lambda/n + z^2/(4*n^2))
#
# }
# garwood is the same as exact, check that!!
# , "garwood" = {
# lwr.ci <- qchisq((1 - conf.level)/2, 2*x)/(2*n)
# upr.ci <- qchisq(1 - (1 - conf.level)/2, 2*(x + 1))/(2*n)
# }
)
ci <- c( est=lambda, lwr.ci=lwr.ci, upr.ci=upr.ci )
if(sides=="left")
ci[3] <- Inf
else if(sides=="right")
ci[2] <- -Inf
return(ci)
}
# handle vectors
# which parameter has the highest dimension
lst <- list(x=x, n=n, conf.level=conf.level, sides=sides, method=method)
maxdim <- max(unlist(lapply(lst, length)))
# recycle all params to maxdim
lgp <- lapply( lst, rep, length.out=maxdim )
res <- sapply(1:maxdim, function(i) iPoissonCI(x=lgp$x[i], n=lgp$n[i],
conf.level=lgp$conf.level[i],
sides=lgp$sides[i],
method=lgp$method[i]))
rownames(res)[1] <- c("est")
lgn <- Recycle(x=if(is.null(names(x))) paste("x", seq_along(x), sep=".") else names(x),
n=if(is.null(names(n))) paste("n", seq_along(n), sep=".") else names(n),
conf.level=conf.level, sides=sides, method=method)
xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) length(unique(x)) != 1)]), 1, paste, collapse=":")
colnames(res) <- xn
return(t(res))
}
QuantileCI <- function(x, probs=seq(0, 1, .25), conf.level = 0.95, sides = c("two.sided", "left", "right"),
na.rm = FALSE, method = c("exact", "boot"), R = 999) {
.QuantileCI <- function(x, prob, conf.level = 0.95, sides = c("two.sided", "left", "right")) {
# Near-symmetric distribution-free confidence interval for a quantile `q`.
# https://stats.stackexchange.com/questions/99829/how-to-obtain-a-confidence-interval-for-a-percentile
# Search over a small range of upper and lower order statistics for the
# closest coverage to 1-alpha (but not less than it, if possible).
n <- length(x)
alpha <- 1- conf.level
if(sides == "two.sided"){
u <- qbinom(p = 1-alpha/2, size = n, prob = prob) + (-2:2) + 1
l <- qbinom(p = alpha/2, size = n, prob = prob) + (-2:2)
u[u > n] <- Inf
l[l < 0] <- -Inf
coverage <- outer(l, u, function(a,b) pbinom(b-1, n, prob = prob) - pbinom(a-1, n, prob=prob))
if (max(coverage) < 1-alpha) i <- which(coverage==max(coverage)) else
i <- which(coverage == min(coverage[coverage >= 1-alpha]))
# minimal difference
i <- i[1]
# order statistics and the actual coverage.
u <- rep(u, each=5)[i]
l <- rep(l, 5)[i]
coverage <- coverage[i]
} else if(sides == "left"){
l <- qbinom(p = alpha, size = n, prob = prob)
u <- Inf
coverage <- 1 - pbinom(q = l-1, size = n, prob = prob)
} else if(sides == "right"){
l <- -Inf
u <- qbinom(p = 1-alpha, size = n, prob = prob)
coverage <- pbinom(q = u, size = n, prob = prob)
}
# get the values
if(prob %nin% c(0,1))
s <- sort(x, partial= c(u, l)[is.finite(c(u, l))])
else
s <- sort(x)
res <- c(lwr.ci=s[l], upr.ci=s[u])
attr(res, "conf.level") <- coverage
if(sides=="left")
res[2] <- Inf
else if(sides=="right")
res[1] <- -Inf
return(res)
}
if (na.rm) x <- na.omit(x)
if(anyNA(x))
stop("missing values and NaN's not allowed if 'na.rm' is FALSE")
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
method <- match.arg(arg=method, choices=c("exact","boot"))
switch( method
, "exact" = { # this is the SAS-way to do it
r <- lapply(probs, function(p) .QuantileCI(x, prob=p, conf.level = conf.level, sides=sides))
coverage <- sapply(r, function(z) attr(z, "conf.level"))
r <- do.call(rbind, r)
attr(r, "conf.level") <- coverage
}
, "boot" = {
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
r <- t(sapply(probs,
function(p) {
boot.med <- boot(x, function(x, d) quantile(x[d], probs=p, na.rm=na.rm), R=R)
boot.ci(boot.med, conf=conf.level, type="basic")[[4]][4:5]
}))
} )
qq <- quantile(x, probs=probs, na.rm=na.rm)
if(length(probs)==1){
res <- c(qq, r)
names(res) <- c("est","lwr.ci","upr.ci")
# report the conf.level which can deviate from the required one
if(method=="exact") attr(res, "conf.level") <- attr(r, "conf.level")
} else {
res <- cbind(qq, r)
colnames(res) <- c("est","lwr.ci","upr.ci")
# report the conf.level which can deviate from the required one
if(method=="exact")
# report coverages for all probs
attr(res, "conf.level") <- attr(r, "conf.level")
}
return( res )
}
# standard error of mean
MeanSE <- function(x, sd = NULL, na.rm = FALSE) {
if(na.rm) x <- na.omit(x)
if(is.null(sd)) s <- sd(x)
s/sqrt(length(x))
}
MeanCIn <- function(ci, sd, interval=c(2, 1e5), conf.level=0.95, norm=FALSE,
tol = .Machine$double.eps^0.5) {
width <- diff(ci)/2
alpha <- (1-conf.level)/2
if(width > sd){
warning("Width of confidence intervall > 2*sd, samplesize n=1 is ok for that case.")
return(1)
} else {
if(norm)
uniroot(f = function(n) sd/sqrt(n) * qnorm(p = 1-alpha) - width,
interval = interval, tol = tol)$root
else
uniroot(f = function(n) (qt(1-alpha, df=n-1) * sd / sqrt(n)) - width,
interval = interval, tol = tol)$root
}
}
MeanDiffCI <- function(x, ...){
UseMethod("MeanDiffCI")
}
MeanDiffCI.formula <- function (formula, data, subset, na.action, ...) {
# this is from t.test.formula
if (missing(formula) || (length(formula) != 3L) || (length(attr(terms(formula[-2L]),
"term.labels")) != 1L))
stop("'formula' missing or incorrect")
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, parent.frame())))
m$data <- as.data.frame(data)
m[[1L]] <- as.name("model.frame")
m$... <- NULL
mf <- eval(m, parent.frame())
DNAME <- paste(names(mf), collapse = " by ")
names(mf) <- NULL
response <- attr(attr(mf, "terms"), "response")
g <- factor(mf[[-response]])
if (nlevels(g) != 2L)
stop("grouping factor must have exactly 2 levels")
DATA <- setNames(split(mf[[response]], g), c("x", "y"))
y <- DoCall("MeanDiffCI", c(DATA, list(...)))
# y$data.name <- DNAME
# if (length(y$estimate) == 2L)
# names(y$estimate) <- paste("mean in group", levels(g))
y
}
MeanDiffCI.default <- function (x, y, method = c("classic", "norm","basic","stud","perc","bca"),
conf.level = 0.95, sides = c("two.sided","left","right"),
paired = FALSE, na.rm = FALSE, R=999, ...) {
if (na.rm) {
x <- na.omit(x)
y <- na.omit(y)
}
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
method <- match.arg(method, c("classic", "norm","basic","stud","perc","bca"))
if(method == "classic"){
a <- t.test(x, y, conf.level = conf.level, paired = paired)
if(paired)
res <- c(meandiff = mean(x - y), lwr.ci = a$conf.int[1], upr.ci = a$conf.int[2])
else
res <- c(meandiff = mean(x) - mean(y), lwr.ci = a$conf.int[1], upr.ci = a$conf.int[2])
} else {
diff.means <- function(d, f){
n <- nrow(d)
gp1 <- 1:table(as.numeric(d[,2]))[1]
m1 <- sum(d[gp1,1] * f[gp1])/sum(f[gp1])
m2 <- sum(d[-gp1,1] * f[-gp1])/sum(f[-gp1])
m1 - m2
}
m <- cbind(c(x,y), c(rep(1,length(x)), rep(2,length(y))))
if(paired)
boot.fun <- boot(x-y, function(d, i) mean(d[i]), R=R, stype="i")
else
boot.fun <- boot(m, diff.means, R=R, stype="f", strata = m[,2])
ci <- boot.ci(boot.fun, conf=conf.level, type=method)
if(method == "norm"){
res <- c(meandiff=boot.fun$t0, lwr.ci=ci[[4]][2], upr.ci=ci[[4]][3])
} else {
res <- c(meandiff=boot.fun$t0, lwr.ci=ci[[4]][4], upr.ci=ci[[4]][5])
}
}
if(sides=="left")
res[3] <- Inf
else if(sides=="right")
res[2] <- -Inf
return(res)
}
# CohenEffectSize <- function(x){
# (C) Antti Arppe 2007-2011
# E-mail: antti.arppe@helsinki.fi
# Cohen's Effect Size (1988)
# e0 <- matrix(,ctable.rows,ctable.cols)
# for(i in 1:ctable.rows)
# for(j in 1:ctable.cols)
# e0[i,j] <- sum.row[i]*sum.col[j]/N
# p0 <- e0/N
# p1 <- ctable/N
# effect.size <- sqrt(sum(((p1-p0)^2)/p0))
# noncentrality <- N*(effect.size^2)
# d.f=(ctable.rows-1)*(ctable.cols-1)
# beta <- pchisq(qchisq(alpha,df=d.f,lower.tail=FALSE),df=d.f,ncp=noncentrality)
# power <- 1-beta
# return(effect.size)
# }
.cohen_d_ci <- function (d, n = NULL, n2 = NULL, n1 = NULL, alpha = 0.05) {
# William Revelle in psych
d2t <- function (d, n = NULL, n2 = NULL, n1 = NULL) {
if (is.null(n1)) {
t <- d * sqrt(n)/2
} else if (is.null(n2)) {
t <- d * sqrt(n1)
} else {
t <- d/sqrt(1/n1 + 1/n2)
}
return(t)
}
t2d <- function (t, n = NULL, n2 = NULL, n1 = NULL) {
if (is.null(n1)) {
d <- 2 * t/sqrt(n)
} else {
if (is.null(n2)) {
d <- t/sqrt(n1)
} else {
d <- t * sqrt(1/n1 + 1/n2)
}
}
return(d)
}
t <- d2t(d = d, n = n, n2 = n2, n1 = n1)
tail <- 1 - alpha/2
ci <- matrix(NA, ncol = 3, nrow = length(d))
for (i in 1:length(d)) {
nmax <- max(c(n/2 + 1, n1 + 1, n1 + n2))
upper <- try(t2d(uniroot(function(x) {
suppressWarnings(pt(q = t[i], df = nmax - 2, ncp = x)) -
alpha/2
}, c(min(-5, -abs(t[i]) * 10), max(5, abs(t[i]) * 10)))$root,
n = n[i], n2 = n2[i], n1 = n1[i]), silent = TRUE)
if (inherits(upper, "try-error")) {
ci[i, 3] <- NA
}
else {
ci[i, 3] <- upper
}
ci[i, 2] <- d[i]
lower.ci <- try(t2d(uniroot(function(x) {
suppressWarnings(pt(q = t[i], df = nmax - 2, ncp = x)) -
tail
}, c(min(-5, -abs(t[i]) * 10), max(5, abs(t[i]) * 10)))$root,
n = n[i], n2 = n2[i], n1 = n1[i]), silent = TRUE)
if (inherits(lower.ci, "try-error")) {
ci[i, 1] <- NA
}
else {
ci[i, 1] <- lower.ci
}
}
colnames(ci) <- c("lower", "effect", "upper")
rownames(ci) <- names(d)
return(ci)
}
CohenD <- function(x, y=NULL, pooled = TRUE, correct = FALSE, conf.level = NA, na.rm = FALSE) {
if (na.rm) {
x <- na.omit(x)
if(!is.null(y)) y <- na.omit(y)
}
if(is.null(y)){ # one sample Cohen d
d <- mean(x) / sd(x)
n <- length(x)
if(!is.na(conf.level)){
# # reference: Smithson Confidence Intervals pp. 36:
# ci <- .nctCI(d / sqrt(n), df = n-1, conf = conf.level)
# res <- c(d=d, lwr.ci=ci[1]/sqrt(n), upr.ci=ci[3]/sqrt(n))
# changed to Revelle 2022-10-22:
ci <- .cohen_d_ci(d = d, n = n, alpha = 1-conf.level)
res <- c(d=d, lwr.ci=ci[1], upr.ci=ci[3])
} else {
res <- d
}
} else {
meanx <- mean(x)
meany <- mean(y)
# ssqx <- sum((x - meanx)^2)
# ssqy <- sum((y - meany)^2)
nx <- length(x)
ny <- length(y)
DF <- nx + ny - 2
d <- (meanx - meany)
if(pooled){
d <- d / sqrt(((nx - 1) * var(x) + (ny - 1) * var(y)) / DF)
}else{
d <- d / sd(c(x, y))
}
# if(unbiased) d <- d * gamma(DF/2)/(sqrt(DF/2) * gamma((DF - 1)/2))
if(correct){ # "Hedges's g"
# Hedges, L. V. & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando, FL: Academic Press.
d <- d * (1 - 3 / ( 4 * (nx + ny) - 9))
}
if(!is.na(conf.level)) {
# old:
# The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009)
## p 238
# ci <- d + c(-1, 1) * sqrt(((nx+ny) / (nx*ny) + .5 * d^2 / DF) * ((nx + ny)/DF)) * qt((1 - conf.level) / 2, DF)
# # supposed to be better, Smithson's version:
# ci <- .nctCI(d / sqrt(nx*ny/(nx+ny)), df = DF, conf = conf.level)
# res <- c(d=d, lwr.ci=ci[1]/sqrt(nx*ny/(nx+ny)), upr.ci=ci[3]/sqrt(nx*ny/(nx+ny)))
# changed to Revelle
ci <- .cohen_d_ci(d, n2 = nx, n1 = ny, alpha = 1-conf.level)
res <- c(d=d, lwr.ci=unname(ci[1]), upr.ci=unname(ci[3]))
} else {
res <- d
}
}
## Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159. Crow, E. L. (1991).
attr(res, "magnitude") <- c("negligible","small","medium","large")[findInterval(abs(d), c(0.2, 0.5, 0.8)) + 1]
return(res)
}
# find non-centrality parameter for the F-distribution
ncparamF <- function(type1, type2, nu1, nu2) {
# author Ali Baharev <ali.baharev at gmail.com>
# Returns the noncentrality parameter of the noncentral F distribution
# if probability of Type I and Type II error, degrees of freedom of the
# numerator and the denominator in the F test statistics are given.
.C("fpow", PACKAGE = "DescTools", as.double(type1), as.double(type2),
as.double(nu1), as.double(nu2), lambda=double(1))$lambda
}
.nctCI <- function(t, df, conf) {
alpha <- 1 - conf
probs <- c(alpha/2, 1 - alpha/2)
ncp <- suppressWarnings(optim(par = 1.1 * rep(t, 2), fn = function(x) {
p <- pt(q = t, df = df, ncp = x)
abs(max(p) - probs[2]) + abs(min(p) - probs[1])
}, control = list(abstol = 0.000000001)))
t_ncp <- unname(sort(ncp$par))
return(t_ncp)
}
# .nctCI <- function(tval.1, df, conf) {
#
# # Function for finding the upper and lower confidence limits for the noncentrality from noncentral t distributions.
# # Especially helpful when forming confidence intervals around the standardized effect size, Cohen's d.
#
# ###################################################################################################################
# # The following code was adapted from code written by Michael Smithson:
# # Australian National University, sometime around the early part of October, 2001
# # Adapted by Joe Rausch & Ken Kelley: University of Notre Dame, in January 2002.
# # Available at: JRausch@nd.edu & KKelley@nd.edu
# ###################################################################################################################
#
#
# # tval.1 is the observed t value, df is the degrees of freedom (group size need not be equal), and conf is simply 1 - alpha
#
# # Result <- matrix(NA,1,4)
# tval <- abs(tval.1)
#
#
# ############################This part Finds the Lower bound for the confidence interval###########################
# ulim <- 1 - (1-conf)/2
#
# # This first part finds a lower value from which to start.
# lc <- c(-tval,tval/2,tval)
# while(pt(tval, df, lc[1])<ulim) {
# lc <- c(lc[1]-tval,lc[1],lc[3])
# }
#
# # This next part finds the lower limit for the ncp.
# diff <- 1
# while(diff > .00000001) {
# if(pt(tval, df, lc[2]) <ulim)
# lc <- c(lc[1],(lc[1]+lc[2])/2,lc[2])
# else lc <- c(lc[2],(lc[2]+lc[3])/2,lc[3])
# diff <- abs(pt(tval,df,lc[2]) - ulim)
# ucdf <- pt(tval,df,lc[2])
# }
# res.1 <- ifelse(tval.1 >= 0,lc[2],-lc[2])
#
# ############################This part Finds the Upper bound for the confidence interval###########################
# llim <- (1-conf)/2
#
# # This first part finds an upper value from which to start.
# uc <- c(tval,1.5*tval,2*tval)
# while(pt(tval,df,uc[3])>llim) {
# uc <- c(uc[1],uc[3],uc[3]+tval)
# }
#
# # This next part finds the upper limit for the ncp.
# diff <- 1
# while(diff > .00000001) {
# if(pt(tval,df,uc[2])<llim)
# uc <- c(uc[1],(uc[1]+uc[2])/2,uc[2])
# else uc <- c(uc[2],(uc[2]+uc[3])/2,uc[3])
# diff <- abs(pt(tval,df,uc[2]) - llim)
# lcdf <- pt(tval,df,uc[2])
# }
# res <- ifelse(tval.1 >= 0,uc[2],-uc[2])
#
#
# #################################This part Compiles the results into a matrix#####################################
#
# return(c(lwr.ci=min(res, res.1), lprob=ucdf, upr.ci=max(res, res.1), uprob=lcdf))
#
# # Result[1,1] <- min(res,res.1)
# # Result[1,2] <- ucdf
# # Result[1,3] <- max(res,res.1)
# # Result[1,4] <- lcdf
# # dimnames(Result) <- list("Values", c("Lower.Limit", "Prob.Low.Limit", "Upper.Limit", "Prob.Up.Limit"))
# # Result
# }
CoefVar <- function (x, ...) {
UseMethod("CoefVar")
}
CoefVar.lm <- function (x, unbiased = FALSE, na.rm = FALSE, ...) {
# source: http://www.ats.ucla.edu/stat/mult_pkg/faq/general/coefficient_of_variation.htm
# In the modeling setting, the CV is calculated as the ratio of the root mean squared error (RMSE)
# to the mean of the dependent variable.
# root mean squared error
rmse <- sqrt(sum(x$residuals^2) / x$df.residual)
res <- rmse / mean(x$model[[1]], na.rm=na.rm)
# This is the same approach as in CoefVar.default, but it's not clear
# if it is correct in the environment of a model
n <- x$df.residual
if (unbiased) {
res <- res * ((1 - (1/(4 * (n - 1))) + (1/n) * res^2) +
(1/(2 * (n - 1)^2)))
}
# if (!is.na(conf.level)) {
# ci <- .nctCI(sqrt(n)/res, df = n - 1, conf = conf.level)
# res <- c(est = res, low.ci = unname(sqrt(n)/ci["upr.ci"]),
# upr.ci = unname(sqrt(n)/ci["lwr.ci"]))
# }
return(res)
}
# interface for lme???
# dependent variable in lme
# dv <- unname(nlme::getResponse(x))
# CoefVar.default <- function (x, weights=NULL, unbiased = FALSE, conf.level = NA, na.rm = FALSE, ...) {
#
# if(is.null(weights)){
# if(na.rm) x <- na.omit(x)
# res <- SD(x) / Mean(x)
# n <- length(x)
#
# }
# else {
# res <- SD(x, weights = weights) / Mean(x, weights = weights)
# n <- sum(weights)
#
# }
#
# if(unbiased) {
# res <- res * ((1 - (1/(4*(n-1))) + (1/n) * res^2)+(1/(2*(n-1)^2)))
# }
#
# if(!is.na(conf.level)){
# ci <- .nctCI(sqrt(n)/res, df = n-1, conf = conf.level)
# res <- c(est=res, low.ci= unname(sqrt(n)/ci["upr.ci"]), upr.ci= unname(sqrt(n)/ci["lwr.ci"]))
# }
#
# return(res)
#
# }
CoefVarCI <- function (K, n, conf.level = 0.95,
sides = c("two.sided", "left", "right"),
method = c("nct","vangel","mckay","verrill","naive")) {
# Description of confidence intervals
# https://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/coefvacl.htm
.iCoefVarCI <- Vectorize(function(K, n, conf.level=0.95,
sides = c("two.sided", "left", "right"),
method = c("vangel","mckay","verrill","nct","naive")) {
method <- match.arg(method)
sides <- match.arg(sides, choices = c("two.sided", "left", "right"),
several.ok = FALSE)
# double alpha in case of one-sided intervals in order to be able
# to generally calculate twosided intervals and select afterwards..
if (sides != "two.sided")
conf.level <- 1 - 2 * (1 - conf.level)
alpha <- 1 - conf.level
df <- n - 1
u1 <- qchisq(1-alpha/2, df)
u2 <- qchisq(alpha/2, df)
switch(method, verrill = {
CI.lower <- 0
CI.upper <- 1
}, vangel = {
CI.lower <- K / sqrt(((u1+2)/n - 1) * K^2 + u1/df)
CI.upper <- K / sqrt(((u2+2)/n - 1) * K^2 + u2/df)
}, mckay = {
CI.lower <- K / sqrt((u1/n - 1) * K^2 + u1/df)
CI.upper <- K / sqrt((u2/n - 1) * K^2 + u2/df)
}, nct = {
ci <- .nctCI(sqrt(n)/K, df = df, conf = conf.level)
CI.lower <- unname(sqrt(n)/ci[2])
CI.upper <- unname(sqrt(n)/ci[1])
}, naive = {
CI.lower <- K * sqrt(df / u1)
CI.upper <- K * sqrt(df / u2)
}
)
ci <- c(est = K,
lwr.ci = CI.lower, # max(0, CI.lower),
upr.ci = CI.upper) # min(1, CI.upper))
if (sides == "left")
ci[3] <- Inf
else if (sides == "right")
ci[2] <- -Inf
return(ci)
})
sides <- match.arg(sides)
method <- match.arg(method)
res <- t(.iCoefVarCI(K=K, n=n, method=method, sides=sides, conf.level = conf.level))
return(res)
}
CoefVar.default <- function (x, weights = NULL, unbiased = FALSE,
na.rm = FALSE, ...) {
if (is.null(weights)) {
if (na.rm)
x <- na.omit(x)
res <- SD(x)/Mean(x)
n <- length(x)
} else {
res <- SD(x, weights = weights)/Mean(x, weights = weights)
n <- sum(weights)
}
if (unbiased) {
res <- res * ((1 - (1/(4 * (n - 1))) + (1/n) * res^2) + (1/(2 * (n - 1)^2)))
}
return(res)
}
# aus agricolae: Variations Koeffizient aus aov objekt
#
# CoefVar.aov <- function(x){
# return(sqrt(sum(x$residual^2) / x$df.residual) / mean(x$fitted.values))
# }
CoefVar.aov <- function (x, unbiased = FALSE, na.rm = FALSE, ...) {
# source: http://www.ats.ucla.edu/stat/mult_pkg/faq/general/coefficient_of_variation.htm
# In the modeling setting, the CV is calculated as the ratio of the root mean squared error (RMSE)
# to the mean of the dependent variable.
# root mean squared error
rmse <- sqrt(sum(x$residuals^2) / x$df.residual)
res <- rmse / mean(x$model[[1]], na.rm=na.rm)
# This is the same approach as in CoefVar.default, but it's not clear
# if it is correct in the enviroment of a model
n <- x$df.residual
if (unbiased) {
res <- res * ((1 - (1/(4 * (n - 1))) + (1/n) * res^2) +
(1/(2 * (n - 1)^2)))
}
# if (!is.na(conf.level)) {
# ci <- .nctCI(sqrt(n)/res, df = n - 1, conf = conf.level)
# res <- c(est = res, low.ci = unname(sqrt(n)/ci["upr.ci"]),
# upr.ci = unname(sqrt(n)/ci["lwr.ci"]))
# }
return(res)
}
VarCI <- function (x, method = c("classic", "bonett", "norm", "basic","stud","perc","bca"),
conf.level = 0.95, sides = c("two.sided","left","right"), na.rm = FALSE, R=999) {
if (na.rm) x <- na.omit(x)
method <- match.arg(method, c("classic","bonett", "norm","basic","stud","perc","bca"))
sides <- match.arg(sides, choices = c("two.sided","left","right"), several.ok = FALSE)
if(sides!="two.sided")
conf.level <- 1 - 2*(1-conf.level)
if(method == "classic"){
df <- length(x) - 1
v <- var(x)
res <- c (var = v, lwr.ci = df * v/qchisq((1 - conf.level)/2, df, lower.tail = FALSE)
, upr.ci = df * v/qchisq((1 - conf.level)/2, df) )
} else if(method=="bonett") {
z <- qnorm(1-(1-conf.level)/2)
n <- length(x)
cc <- n/(n-z)
v <- var(x)
mtr <- mean(x, trim = 1/(2*(n-4)^0.5))
m <- mean(x)
gam4 <- n * sum((x-mtr)^4) / (sum((x-m)^2))^2
se <- cc * sqrt((gam4 - (n-3)/n)/(n-1))
lci <- exp(log(cc * v) - z*se)
uci <- exp(log(cc * v) + z*se)
res <- c(var=v, lwr.ci=lci, upr.ci=uci)
} else {
boot.fun <- boot(x, function(x, d) var(x[d], na.rm=na.rm), R=R)
ci <- boot.ci(boot.fun, conf=conf.level, type=method)
if(method == "norm"){
res <- c(var=boot.fun$t0, lwr.ci=ci[[4]][2], upr.ci=ci[[4]][3])
} else {
res <- c(var=boot.fun$t0, lwr.ci=ci[[4]][4], upr.ci=ci[[4]][5])
}
}
if(sides=="left")
res[3] <- Inf
else if(sides=="right")
res[2] <- 0
return(res)
}
## stats: Lorenz, <- & ineq ====
Lc <- function(x, ...)
UseMethod("Lc")
Lc.formula <- function(formula, data, subset, na.action, ...) {
# this is taken basically from wilcox.test.formula
if (missing(formula) || (length(formula) != 3L) || (length(attr(terms(formula[-2L]),
"term.labels")) != 1L))
stop("'formula' missing or incorrect")
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, parent.frame())))
m$data <- as.data.frame(data)
m[[1L]] <- as.name("model.frame")
m$... <- NULL
mf <- eval(m, parent.frame())
# mf$na.action <- substitute(na.action)
# DNAME <- paste(names(mf), collapse = " by ")
#
# DATA <- list(table(mf))
# do.call("Lc", c(DATA, list(...)))
drop <- TRUE
# mf <- model.frame(x, data)
x <- split(x = mf[,1], f = mf[,2], drop=drop, ...)
res <- lapply(x, FUN = "Lc", ...)
class(res) <- "Lclist"
return(res)
}
Lc.default <- function(x, n = rep(1, length(x)), na.rm = FALSE, ...) {
xx <- x
nn <- n
g <- Gini(x, n, na.rm=na.rm)
if(na.rm) x <- na.omit(x)
if (any(is.na(x)) || any(x < 0)) return(NA_real_)
k <- length(x)
o <- order(x)
x <- x[o]
n <- n[o]
x <- n*x
p <- cumsum(n)/sum(n)
L <- cumsum(x)/sum(x)
p <- c(0,p)
L <- c(0,L)
L2 <- L * mean(x)
Lc <- list(p, L, L2, g, xx, nn)
names(Lc) <- c("p", "L", "L.general", "Gini", "x", "n")
class(Lc) <- "Lc"
# no plot anymore, we have plot(lc) and Desc(lc, plotit=TRUE)
# if(plot) plot(Lc)
Lc
}
plot.Lc <- function(x, general=FALSE, lwd=2, type="l", xlab="p", ylab="L(p)",
main="Lorenz curve", las=1, pch=NA, ...) {
if(!general)
L <- x$L
else
L <- x$L.general
plot(x$p, L, type=type, main=main, lwd=lwd, xlab=xlab, ylab=ylab, xaxs="i",
yaxs="i", las=las, ...)
abline(0, max(L))
if(!is.na(pch)){
opar <- par(xpd=TRUE)
on.exit(par(opar))
points(x$p, L, pch=pch, ...)
}
}
lines.Lc <- function(x, general=FALSE, lwd=2, conf.level = NA, args.cband = NULL, ...) {
# Lc.boot.ci <- function(x, conf.level=0.95, n=1000){
#
# x <- rep(x$x, times=x$n)
# m <- matrix(sapply(1:n, function(i) sample(x, replace = TRUE)), nrow=length(x))
#
# lst <- apply(m, 2, Lc)
# list(x=c(lst[[1]]$p, rev(lst[[1]]$p)),
# y=c(apply(do.call(rbind, lapply(lst, "[[", "L")), 2, quantile, probs=(1-conf.level)/2),
# rev(apply(do.call(rbind, lapply(lst, "[[", "L")), 2, quantile, probs=1-(1-conf.level)/2)))
# )
# }
#
#
if(!general)
L <- x$L
else
L <- x$L.general
if (!(is.na(conf.level) || identical(args.cband, NA)) ) {
args.cband1 <- list(col = SetAlpha(DescToolsOptions("col")[1], 0.12), border = NA)
if (!is.null(args.cband))
args.cband1[names(args.cband)] <- args.cband
# ci <- Lc.boot.ci(x, conf.level=conf.level) # Vertrauensband
ci <- predict(object=x, conf.level=conf.level, general=general)
do.call("DrawBand", c(args.cband1, list(x=c(ci$p, rev(ci$p))),
list(y=c(ci$lci, rev(ci$uci)))))
}
lines(x$p, L, lwd=lwd, ...)
}
plot.Lclist <- function(x, col=1, lwd=2, lty=1, main = "Lorenz curve",
xlab="p", ylab="L(p)", ...){
# Recycle arguments
lgp <- Recycle(x=seq_along(x), col=col, lwd=lwd, lty=lty)
plot(x[[1]], col=lgp$col[1], lwd=lgp$lwd[1], lty=lgp$lty[1], main=main, xlab=xlab, ylab=ylab, ...)
for(i in 2L:length(x)){
lines(x[[i]], col=lgp$col[i], lwd=lgp$lwd[i], lty=lgp$lty[i])
}
}
predict.Lc <- function(object, newdata, conf.level=NA, general=FALSE, n=1000, ...){
confint.Lc <- function(object, conf.level = 0.95, general=FALSE, n=1000, ...){
x <- rep(object$x, times=object$n)
m <- replicate(n = n, sample(x, replace = TRUE))
lst <- apply(m, 2, Lc)
list(x=lst[[1]]$p,
lci=apply(do.call(rbind, lapply(lst, "[[", ifelse(general, "L.general", "L"))), 2, quantile, probs=(1-conf.level)/2),
uci=apply(do.call(rbind, lapply(lst, "[[", ifelse(general, "L.general", "L"))), 2, quantile, probs=1-(1-conf.level)/2)
)
}
if(!general)
L <- object$L
else
L <- object$L.general
if(missing(newdata)){
newdata <- object$p
res <- data.frame(p=object$p, L=L)
} else {
res <- do.call(data.frame, approx(x=object$p, y=L, xout=newdata))
colnames(res) <- c("p", "L")
}
if(!identical(conf.level, NA)){
ci <- confint.Lc(object, conf.level=conf.level, general=general, n=n)
lci <- approx(x=ci$x, y=ci$lci, xout=newdata)
uci <- approx(x=ci$x, y=ci$uci, xout=newdata)
res <- data.frame(res, lci=lci$y, uci=uci$y)
}
res
}
# Original Zeileis:
# Gini <- function(x)
# {
# n <- length(x)
# x <- sort(x)
# G <- sum(x * 1:n)
# G <- 2*G/(n*sum(x))
# G - 1 - (1/n)
# }
# other:
# http://rss.acs.unt.edu/Rdoc/library/reldist/html/gini.html
# http://finzi.psych.upenn.edu/R/library/dplR/html/gini.coef.html
# Gini <- function(x, n = rep(1, length(x)), unbiased = TRUE, conf.level = NA, R = 1000, type = "bca", na.rm = FALSE) {
#
# # cast to numeric, as else sum(x * 1:n) might overflow for integers
# # http://stackoverflow.com/questions/39579029/integer-overflow-error-using-gini-function-of-package-desctools
# x <- as.numeric(x)
#
# x <- rep(x, n) # same handling as Lc
# if(na.rm) x <- na.omit(x)
# if (any(is.na(x)) || any(x < 0)) return(NA_real_)
#
# i.gini <- function (x, unbiased = TRUE){
# n <- length(x)
# x <- sort(x)
#
# res <- 2 * sum(x * 1:n) / (n*sum(x)) - 1 - (1/n)
# if(unbiased) res <- n / (n - 1) * res
#
# # limit Gini to 0 here, if negative values appear, which is the case with
# # Gini( c(10,10,10))
# return( pmax(0, res))
#
# # other guy out there:
# # N <- if (unbiased) n * (n - 1) else n * n
# # dsum <- drop(crossprod(2 * 1:n - n - 1, x))
# # dsum / (mean(x) * N)
# # is this slower, than above implementation??
# }
#
# if(is.na(conf.level)){
# res <- i.gini(x, unbiased = unbiased)
#
# } else {
# # adjusted bootstrap percentile (BCa) interval
# boot.gini <- boot(x, function(x, d) i.gini(x[d], unbiased = unbiased), R=R)
# ci <- boot.ci(boot.gini, conf=conf.level, type=type)
# res <- c(gini=boot.gini$t0, lwr.ci=ci[[4]][4], upr.ci=ci[[4]][5])
# }
#
# return(res)
#
# }
# recoded for better support weights 2022-09-14
Gini <- function(x, weights=NULL, unbiased=TRUE,
conf.level = NA, R = 10000, type = "bca", na.rm=FALSE) {
# https://core.ac.uk/download/pdf/41339501.pdf
if (is.null(weights)) {
weights <- rep(1, length(x))
}
if (na.rm){
na <- (is.na(x) | is.na(weights))
x <- x[!na]
weights <- weights[!na]
}
if (any(is.na(x)) || any(x < 0))
return(NA_real_)
i.gini <- function(x, w, unbiased=FALSE) {
w <- w/sum(w)
x <- x[id <- order(x)]
w <- w[id]
f.hat <- w / 2 + c(0, head(cumsum(w), -1))
wm <- Mean(x, w)
res <- 2 / wm * sum(w * (x - wm) * (f.hat - Mean(f.hat, w)))
if(unbiased)
res <- res * 1/(1 - sum(w^2))
return(res)
}
if (is.na(conf.level)) {
res <- i.gini(x, weights, unbiased = unbiased)
} else {
boot.gini <- boot(data = x,
statistic = function(z, i, u, unbiased)
i.gini(x = z[i], w = u[i], unbiased = unbiased),
R=R, u=weights, unbiased=unbiased)
ci <- boot.ci(boot.gini, conf = conf.level, type = type)
res <- c(gini = boot.gini$t0, lwr.ci = ci[[4]][4], upr.ci = ci[[4]][5])
}
return(res)
}
GiniSimpson <- function(x, na.rm = FALSE) {
# referenz: Sachs, Angewandte Statistik, S. 57
# example:
# x <- as.table(c(69,17,7,62))
# rownames(x) <- c("A","B","AB","0")
# GiniSimpson(x)
if(!is.factor(x)){
warning("x is not a factor!")
return(NA)
}
if(na.rm) x <- na.omit(x)
ptab <- prop.table(table(x))
return(sum(ptab*(1-ptab)))
}
GiniDeltas <- function (x, na.rm = FALSE) {
# Deltas (2003, DOI:10.1162/rest.2003.85.1.226).
if (!is.factor(x)) {
warning("x is not a factor!")
return(NA)
}
if (na.rm)
x <- na.omit(x)
p <- prop.table(table(x))
sum(p * (1 - p)) * length(p)/(length(p) - 1)
}
HunterGaston <- function(x, na.rm = FALSE){
# Hunter-Gaston index (Hunter & Gaston, 1988, DOI:10.1128/jcm.26.11.2465-2466.1988)
# Credits to Wim Bernasco
# https://github.com/AndriSignorell/DescTools/issues/120
# see: vegan::simpson.unb(BCI)
if (is.factor(x) | is.character(x)) {
if (na.rm)
x <- na.omit(x)
tt <- table(x)
} else {
tt <- x
}
sum(tt * (tt - 1)) / (sum(tt) * (sum(tt) - 1))
# shouldn't this be:
# 1 - sum(tt * (tt - 1)) / (sum(tt) * (sum(tt) - 1))
# ???
}
Atkinson <- function(x, n = rep(1, length(x)), parameter = 0.5, na.rm = FALSE) {
x <- rep(x, n) # same handling as Lc and Gini
if(na.rm) x <- na.omit(x)
if (any(is.na(x)) || any(x < 0)) return(NA_real_)
if(is.null(parameter)) parameter <- 0.5
if(parameter==1)
A <- 1 - (exp(mean(log(x)))/mean(x))
else
{
x <- (x/mean(x))^(1-parameter)
A <- 1 - mean(x)^(1/(1-parameter))
}
A
}
Herfindahl <- function(x, n = rep(1, length(x)), parameter=1, na.rm = FALSE) {
x <- rep(x, n) # same handling as Lc and Gini
if(na.rm) x <- na.omit(x)
if (any(is.na(x)) || any(x < 0)) return(NA_real_)
if(is.null(parameter))
m <- 1
else
m <- parameter
Herf <- x/sum(x)
Herf <- Herf^(m+1)
Herf <- sum(Herf)^(1/m)
Herf
}
Rosenbluth <- function(x, n = rep(1, length(x)), na.rm = FALSE) {
x <- rep(x, n) # same handling as Lc and Gini
if(na.rm) x <- na.omit(x)
if (any(is.na(x)) || any(x < 0)) return(NA_real_)
n <- length(x)
x <- sort(x)
HT <- (n:1)*x
HT <- 2*sum(HT/sum(x))
HT <- 1/(HT-1)
HT
}
###
## stats: assocs etc. ====
CutAge <- function(x, from=0, to=90, by=10, right=FALSE, ordered_result=TRUE, ...){
cut(x, breaks = c(seq(from, to, by), Inf),
right=right, ordered_result = ordered_result, ...)
}
CutQ <- function(x, breaks=quantile(x, seq(0, 1, by=0.25), na.rm=TRUE),
labels=NULL, na.rm = FALSE, ...){
# old version:
# cut(x, breaks=probsile(x, breaks=probs, na.rm = na.rm), include.lowest=TRUE, labels=labels)
# $Id: probscut.R 1431 2010-04-28 17:23:08Z ggrothendieck2 $
# from gtools
if(na.rm) x <- na.omit(x)
if(length(breaks)==1 && IsWhole(breaks))
breaks <- quantile(x, seq(0, 1, by = 1/breaks), na.rm = TRUE)
if(is.null(labels)) labels <- gettextf("Q%s", 1:(length(breaks)-1))
# probs <- quantile(x, probs)
dups <- duplicated(breaks)
if(any(dups)) {
flag <- x %in% unique(breaks[dups])
retval <- ifelse(flag, paste("[", as.character(x), "]", sep=''), NA)
uniqs <- unique(breaks)
# move cut points over a bit...
reposition <- function(cut) {
flag <- x>=cut
if(sum(flag)==0)
return(cut)
else
return(min(x[flag]))
}
newprobs <- sapply(uniqs, reposition)
retval[!flag] <- as.character(cut(x[!flag], breaks=newprobs, include.lowest=TRUE,...))
levs <- unique(retval[order(x)]) # ensure factor levels are
# properly ordered
retval <- factor(retval, levels=levs)
## determine open/closed interval ends
mkpairs <- function(x) # make table of lower, upper
sapply(x,
function(y) if(length(y)==2) y[c(2,2)] else y[2:3]
)
pairs <- mkpairs(strsplit(levs, '[^0-9+\\.\\-]+'))
rownames(pairs) <- c("lower.bound","upper.bound")
colnames(pairs) <- levs
closed.lower <- rep(FALSE, ncol(pairs)) # default lower is open
closed.upper <- rep(TRUE, ncol(pairs)) # default upper is closed
closed.lower[1] <- TRUE # lowest interval is always closed
for(i in 2:ncol(pairs)) # open lower interval if above singlet
if(pairs[1,i]==pairs[1,i-1] && pairs[1,i]==pairs[2,i-1])
closed.lower[i] <- FALSE
for(i in 1:(ncol(pairs)-1)) # open upper interval if below singlet
if(pairs[2,i]==pairs[1,i+1] && pairs[2,i]==pairs[2,i+1])
closed.upper[i] <- FALSE
levs <- ifelse(pairs[1,]==pairs[2,],
pairs[1,],
paste(ifelse(closed.lower,"[","("),
pairs[1,],
",",
pairs[2,],
ifelse(closed.upper,"]",")"),
sep='')
)
levels(retval) <- levs
} else
retval <- cut( x, breaks, include.lowest=TRUE, labels=labels, ... )
return(retval)
}
# Phi-Koeff
Phi <- function (x, y = NULL, ...) {
if(!is.null(y)) x <- table(x, y, ...)
# when computing phi, note that Yates' correction to chi-square must not be used.
as.numeric( sqrt( suppressWarnings(chisq.test(x, correct=FALSE)$statistic) / sum(x) ) )
# should we implement: ??
# following http://technology.msb.edu/old/training/statistics/sas/books/stat/chap26/sect19.htm#idxfrq0371
# (Liebetrau 1983)
# this makes phi -1 < phi < 1 for 2x2 tables (same for CramerV)
# (prod(diag(x)) - prod(diag(Rev(x, 2)))) / sqrt(prod(colSums(x), rowSums(x)))
}
# Kontingenz-Koeffizient
ContCoef <- function(x, y = NULL, correct = FALSE, ...) {
if(!is.null(y)) x <- table(x, y, ...)
chisq <- suppressWarnings(chisq.test(x, correct = FALSE)$statistic)
cc <- as.numeric( sqrt( chisq / ( chisq + sum(x)) ))
if(correct) { # Sakoda's adjusted Pearson's C
k <- min(nrow(x),ncol(x))
cc <- cc/sqrt((k-1)/k)
}
return(cc)
}
.ncchisqCI <- function(chisq, df, conf) {
alpha <- 1 - conf
probs <- c(alpha/2, 1 - alpha/2)
ncp <- suppressWarnings(optim(par = 1.1 * rep(chisq, 2), fn = function(x) {
p <- pchisq(q = chisq, df = df, ncp = x)
abs(max(p) - probs[2]) + abs(min(p) - probs[1])
}, control = list(abstol = 0.000000001)))
chisq_ncp <- unname(sort(ncp$par))
return(chisq_ncp)
}
CramerV <- function(x, y = NULL, conf.level = NA,
method = c("ncchisq", "ncchisqadj", "fisher", "fisheradj"),
correct=FALSE, ...){
if(!is.null(y)) x <- table(x, y, ...)
# CIs and power for the noncentral chi-sq noncentrality parameter (ncp):
# The function lochi computes the lower CI limit and hichi computes the upper limit.
# Both functions take 3 arguments: observed chi-sq, df, and confidence level.
# author: Michael Smithson
# http://psychology3.anu.edu.au/people/smithson/details/CIstuff/Splusnonc.pdf
# see also: MBESS::conf.limits.nc.chisq, Ken Kelly
lochi <- function(chival, df, conf) {
# we don't have lochi for chival==0
# optimize would report minval = maxval
if(chival==0) return(NA)
ulim <- 1 - (1-conf)/2
# This first part finds a lower value from which to start.
lc <- c(.001, chival/2, chival)
while(pchisq(chival, df, lc[1]) < ulim) {
if(pchisq(chival, df) < ulim)
return(c(0, pchisq(chival, df)))
lc <- c(lc[1]/4, lc[1], lc[3])
}
# This next part finds the lower limit for the ncp.
diff <- 1
while(diff > .00001) {
if(pchisq(chival, df, lc[2]) < ulim)
lc <- c(lc[1],(lc[1]+lc[2])/2, lc[2])
else lc <- c(lc[2], (lc[2]+lc[3])/2, lc[3])
diff <- abs(pchisq(chival, df, lc[2]) - ulim)
ucdf <- pchisq(chival, df, lc[2])
}
c(lc[2], ucdf)
}
hichi <- function(chival,df,conf) {
# we don't have hichi for chival==0
if(chival==0) return(NA)
# This first part finds upper and lower startinig values.
uc <- c(chival, 2*chival, 3*chival)
llim <- (1-conf)/2
while(pchisq(chival, df, uc[1]) < llim) {
uc <- c(uc[1]/4,uc[1],uc[3])
}
while(pchisq(chival,df,uc[3])>llim) {
uc <- c(uc[1],uc[3],uc[3]+chival)
}
# This next part finds the upper limit for the ncp.
diff <- 1
while(diff > .00001) {
if(pchisq(chival, df, uc[2]) < llim)
uc <- c(uc[1], (uc[1] + uc[2]) / 2, uc[2])
else uc <- c(uc[2], (uc[2] + uc[3]) / 2, uc[3])
diff <- abs(pchisq(chival, df, uc[2]) - llim)
lcdf <- pchisq(chival, df, uc[2])
}
c(uc[2], lcdf)
}
# Remark Andri 18.12.2014:
# lochi and hichi could be replaced with:
# optimize(function(x) abs(pchisq(chival, DF, x) - (1-(1-conf.level)/2)), c(0, chival))
# optimize(function(x) abs(pchisq(chival, DF, x) - (1-conf.level)/2), c(0, 3*chival))
#
# ... which would run ~ 25% faster and be more exact
# what can go wrong while calculating chisq.stat?
# we don't need test results here, so we suppress those warnings
chisq.hat <- suppressWarnings(chisq.test(x, correct = FALSE)$statistic)
df <- prod(dim(x)-1)
n <- sum(x)
if(correct){
# Bergsma, W, A bias-correction for Cramer's V and Tschuprow's T
# September 2013Journal of the Korean Statistical Society 42(3)
# DOI: 10.1016/j.jkss.2012.10.002
phi.hat <- chisq.hat / n
v <- as.numeric(sqrt(max(0, phi.hat - df/(n-1)) /
(min(sapply(dim(x), function(i) i - 1 / (n-1) * (i-1)^2) - 1))))
} else {
v <- as.numeric(sqrt(chisq.hat/(n * (min(dim(x)) - 1))))
}
if (is.na(conf.level)) {
res <- v
} else {
switch(match.arg(method),
ncchisq={
ci <- c(lochi(chisq.hat, df, conf.level)[1], hichi(chisq.hat, df, conf.level)[1])
# corrected by michael smithson, 17.5.2014:
# ci <- unname(sqrt( (ci + df) / (sum(x) * (min(dim(x)) - 1)) ))
ci <- unname(sqrt( (ci) / (n * (min(dim(x)) - 1)) ))
},
ncchisqadj={
ci <- c(lochi(chisq.hat, df, conf.level)[1] + df, hichi(chisq.hat, df, conf.level)[1] + df)
# corrected by michael smithson, 17.5.2014:
# ci <- unname(sqrt( (ci + df) / (sum(x) * (min(dim(x)) - 1)) ))
ci <- unname(sqrt( (ci) / (n * (min(dim(x)) - 1)) ))
},
fisher={
se <- 1 / sqrt(n-3) * qnorm(1-(1-conf.level)/2)
ci <- tanh(atanh(v) + c(-se, se))
},
fisheradj={
se <- 1 / sqrt(n-3) * qnorm(1-(1-conf.level)/2)
# bias correction
adj <- 0.5 * v / (n-1)
ci <- tanh(atanh(v) + c(-se, se) + adj)
})
# "Cram\u00E9r's association coefficient"
res <- c("Cramer V"=v, lwr.ci=max(0, ci[1]), upr.ci=min(1, ci[2]))
}
return(res)
}
YuleQ <- function(x, y = NULL, ...){
if(!is.null(y)) x <- table(x, y, ...)
# allow only 2x2 tables
stopifnot(prod(dim(x)) == 4 || length(x) == 4)
a <- x[1,1]
b <- x[1,2]
c <- x[2,1]
d <- x[2,2]
return((a*d- b*c)/(a*d + b*c)) #Yule Q
}
YuleY <- function(x, y = NULL, ...){
if(!is.null(y)) x <- table(x, y, ...)
# allow only 2x2 tables
stopifnot(prod(dim(x)) == 4 || length(x) == 4)
a <- x[1,1]
b <- x[1,2]
c <- x[2,1]
d <- x[2,2]
return((sqrt(a*d) - sqrt(b*c))/(sqrt(a*d)+sqrt(b*c))) # YuleY
}
TschuprowT <- function(x, y = NULL, correct = FALSE, ...){
if(!is.null(y)) x <- table(x, y, ...)
# Tschuprow, A. A. (1939) Principles of the Mathematical Theory of Correlation; translated by M. Kantorowitsch. W. Hodge & Co.
# http://en.wikipedia.org/wiki/Tschuprow's_T
# Hartung S. 451
# what can go wrong while calculating chisq.stat?
# we don't need test results here, so we suppress those warnings
chisq.hat <- suppressWarnings(chisq.test(x, correct = FALSE)$statistic)
n <- sum(x)
df <- prod(dim(x)-1)
if(correct) {
# Bergsma, W, A bias-correction for Cramer's V and Tschuprow's T
# September 2013Journal of the Korean Statistical Society 42(3)
# DOI: 10.1016/j.jkss.2012.10.002
# see also CramerV
phi.hat <- chisq.hat / n
as.numeric(sqrt(max(0, phi.hat - df/(n-1)) /
(sqrt(prod(sapply(dim(x), function(i) i - 1 / (n-1) * (i-1)^2) - 1)))))
} else {
as.numeric( sqrt(chisq.hat/(n * sqrt(df))))
}
}
# based on Kappa from library(vcd)
# author: David Meyer
# see also: kappa in library(psych)
CohenKappa <- function (x, y = NULL,
weights = c("Unweighted", "Equal-Spacing", "Fleiss-Cohen"),
conf.level = NA, ...) {
if (is.character(weights))
weights <- match.arg(weights)
if (!is.null(y)) {
# we can not ensure a reliable weighted kappa for 2 factors with different levels
# so refuse trying it... (unweighted is no problem)
if (!identical(weights, "Unweighted"))
stop("Vector interface for weighted Kappa is not supported. Provide confusion matrix.")
# x and y must have the same levels in order to build a symmetric confusion matrix
x <- factor(x)
y <- factor(y)
lvl <- unique(c(levels(x), levels(y)))
x <- factor(x, levels = lvl)
y <- factor(y, levels = lvl)
x <- table(x, y, ...)
} else {
d <- dim(x)
if (d[1L] != d[2L])
stop("x must be square matrix if provided as confusion matrix")
}
d <- diag(x)
n <- sum(x)
nc <- ncol(x)
colFreqs <- colSums(x)/n
rowFreqs <- rowSums(x)/n
kappa <- function(po, pc) {
(po - pc)/(1 - pc)
}
std <- function(p, pc, k, W = diag(1, ncol = nc, nrow = nc)) {
sqrt((sum(p * sweep(sweep(W, 1, W %*% colSums(p) * (1 - k)),
2, W %*% rowSums(p) * (1 - k))^2) -
(k - pc * (1 - k))^2) / crossprod(1 - pc)/n)
}
if(identical(weights, "Unweighted")) {
po <- sum(d)/n
pc <- as.vector(crossprod(colFreqs, rowFreqs))
k <- kappa(po, pc)
s <- as.vector(std(x/n, pc, k))
} else {
# some kind of weights defined
W <- if (is.matrix(weights))
weights
else if (weights == "Equal-Spacing")
1 - abs(outer(1:nc, 1:nc, "-"))/(nc - 1)
else # weights == "Fleiss-Cohen"
1 - (abs(outer(1:nc, 1:nc, "-"))/(nc - 1))^2
po <- sum(W * x)/n
pc <- sum(W * colFreqs %o% rowFreqs)
k <- kappa(po, pc)
s <- as.vector(std(x/n, pc, k, W))
}
if (is.na(conf.level)) {
res <- k
} else {
ci <- k + c(1, -1) * qnorm((1 - conf.level)/2) * s
res <- c(kappa = k, lwr.ci = ci[1], upr.ci = ci[2])
}
return(res)
}
# KappaTest <- function(x, weights = c("Equal-Spacing", "Fleiss-Cohen"), conf.level = NA) {
# to do, idea is to implement a Kappa test for H0: kappa = 0 as in
# http://support.sas.com/documentation/cdl/en/statugfreq/63124/PDF/default/statugfreq.pdf, pp. 1687
# print( "still to do...." )
# }
KappaM <- function(x, method = c("Fleiss", "Conger", "Light"), conf.level = NA) {
# ratings <- as.matrix(na.omit(x))
#
# ns <- nrow(ratings)
# nr <- ncol(ratings)
#
# # Build table
# lev <- levels(as.factor(ratings))
#
# for (i in 1:ns) {
# frow <- factor(ratings[i,],levels=lev)
#
# if (i==1)
# ttab <- as.numeric(table(frow))
# else
# ttab <- rbind(ttab, as.numeric(table(frow)))
# }
#
# ttab <- matrix(ttab, nrow=ns)
# we have not factors for matrices, but we need factors below...
if(is.matrix(x))
x <- as.data.frame(x)
x <- na.omit(x)
ns <- nrow(x)
nr <- ncol(x)
# find all levels in the data (data.frame)
lev <- levels(factor(unlist(x)))
# apply the same levels to all variables and switch to integer matrix
xx <- do.call(cbind, lapply(x, factor, levels=lev))
ttab <- apply(Abind(lapply(as.data.frame(xx), function(z) Dummy(z, method="full", levels=seq_along(lev))), along = 3),
c(1,2), sum)
agreeP <- sum((rowSums(ttab^2)-nr)/(nr*(nr-1))/ns)
switch( match.arg(method, choices= c("Fleiss", "Conger", "Light"))
, "Fleiss" = {
chanceP <- sum(colSums(ttab)^2)/(ns*nr)^2
value <- (agreeP - chanceP)/(1 - chanceP)
pj <- colSums(ttab)/(ns*nr)
qj <- 1-pj
varkappa <- (2/(sum(pj*qj)^2*(ns*nr*(nr-1))))*(sum(pj*qj)^2-sum(pj*qj*(qj-pj)))
SEkappa <- sqrt(varkappa)
ci <- value + c(1,-1) * qnorm((1-conf.level)/2) * SEkappa
}
, "Conger" = {
# for (i in 1:nr) {
# rcol <- factor(x[,i],levels=lev)
#
# if (i==1)
# rtab <- as.numeric(table(rcol))
# else
# rtab <- rbind(rtab, as.numeric(table(rcol)))
# }
rtab <- apply(Abind(lapply(as.data.frame(t(xx)), function(z) Dummy(z, method="full", levels=seq_along(lev))), along = 3),
c(1,2), sum)
rtab <- rtab/ns
chanceP <- sum(colSums(ttab)^2)/(ns*nr)^2 - sum(apply(rtab, 2, var)*(nr-1)/nr)/(nr-1)
value <- (agreeP - chanceP)/(1 - chanceP)
# we have not SE for exact Kappa value
ci <- c(NA, NA)
}
, "Light" = {
m <- DescTools::PairApply(x, DescTools::CohenKappa, symmetric=TRUE)
value <- mean(m[upper.tri(m)])
levlen <- length(lev)
for (nri in 1:(nr - 1)) for (nrj in (nri + 1):nr) {
for (i in 1:levlen) for (j in 1:levlen) {
if (i != j) {
r1i <- sum(x[, nri] == lev[i])
r2j <- sum(x[, nrj] == lev[j])
if (!exists("dis"))
dis <- r1i * r2j
else dis <- c(dis, r1i * r2j)
}
}
if (!exists("disrater"))
disrater <- sum(dis)
else disrater <- c(disrater, sum(dis))
rm(dis)
}
B <- length(disrater) * prod(disrater)
chanceP <- 1 - B/ns^(choose(nr, 2) * 2)
varkappa <- chanceP/(ns * (1 - chanceP))
SEkappa <- sqrt(varkappa)
ci <- value + c(1,-1) * qnorm((1-conf.level)/2) * SEkappa
}
)
if (is.na(conf.level)) {
res <- value
} else {
res <- c("kappa"=value, lwr.ci=ci[1], upr.ci=ci[2])
}
return(res)
}
Agree <- function(x, tolerance = 0, na.rm = FALSE) {
x <- as.matrix(x)
if(na.rm) x <- na.omit(x)
if(anyNA(x)) return(NA)
ns <- nrow(x)
nr <- ncol(x)
if (is.numeric(x)) {
rangetab <- apply(x, 1, max) - apply(x, 1, min)
coeff <- sum(rangetab <= tolerance)/ns
} else {
rangetab <- as.numeric(sapply(apply(x, 1, table), length))
coeff <- (sum(rangetab == 1)/ns)
tolerance <- 0
}
rval <- coeff
attr(rval, c("subjects")) <- ns
attr(rval, c("raters")) <- nr
return(rval)
}
# ICC(ratings)
# ICC_(ratings, type="ICC3", conf.level=0.95)
# ICC_(ratings, type="all", conf.level=0.95)
ICC <- function(x, type=c("all", "ICC1","ICC2","ICC3","ICC1k","ICC2k","ICC3k"), conf.level = NA, na.rm = FALSE) {
ratings <- as.matrix(x)
if(na.rm) ratings <- na.omit(ratings)
ns <- nrow(ratings)
nr <- ncol(ratings)
x.s <- stack(data.frame(ratings))
x.df <- data.frame(x.s, subs = rep(paste("S", 1:ns, sep = ""), nr))
s.aov <- summary(aov(values ~ subs + ind, data=x.df))
stats <- matrix(unlist(s.aov), ncol=3, byrow=TRUE)
MSB <- stats[3,1]
MSW <- (stats[2,2] + stats[2,3])/(stats[1,2] + stats[1,3])
MSJ <- stats[3,2]
MSE <- stats[3,3]
ICC1 <- (MSB- MSW)/(MSB+ (nr-1)*MSW)
ICC2 <- (MSB- MSE)/(MSB + (nr-1)*MSE + nr*(MSJ-MSE)/ns)
ICC3 <- (MSB - MSE)/(MSB+ (nr-1)*MSE)
ICC12 <- (MSB-MSW)/(MSB)
ICC22 <- (MSB- MSE)/(MSB +(MSJ-MSE)/ns)
ICC32 <- (MSB-MSE)/MSB
#find the various F values from Shrout and Fleiss
F11 <- MSB/MSW
df11n <- ns-1
df11d <- ns*(nr-1)
p11 <- 1 - pf(F11, df11n, df11d)
F21 <- MSB/MSE
df21n <- ns-1
df21d <- (ns-1)*(nr-1)
p21 <- 1-pf(F21, df21n, df21d)
F31 <- F21
# results <- t(results)
results <- data.frame(matrix(NA, ncol=8, nrow=6))
colnames(results ) <- c("type", "est","F-val","df1","df2","p-val","lwr.ci","upr.ci")
rownames(results) <- c("Single_raters_absolute","Single_random_raters","Single_fixed_raters", "Average_raters_absolute","Average_random_raters","Average_fixed_raters")
results[,1] = c("ICC1","ICC2","ICC3","ICC1k","ICC2k","ICC3k")
results[,2] = c(ICC1, ICC2, ICC3, ICC12, ICC22, ICC32)
results[1,3] <- results[4,3] <- F11
results[2,3] <- F21
results[3,3] <- results[6,3] <- results[5,3] <- F31 <- F21
results[5,3] <- F21
results[1,4] <- results[4,4] <- df11n
results[1,5] <- results[4,5] <- df11d
results[1,6] <- results[4,6] <- p11
results[2,4] <- results[3,4] <- results[5,4] <- results[6,4] <- df21n
results[2,5] <- results[3,5] <- results[5,5] <- results[6,5] <- df21d
results[2,6] <- results[5,6] <- results[3,6] <- results[6,6] <- p21
#now find confidence limits
#first, the easy ones
alpha <- 1 - conf.level
F1L <- F11 / qf(1-alpha/2, df11n, df11d)
F1U <- F11 * qf(1-alpha/2, df11d, df11n)
L1 <- (F1L-1) / (F1L + (nr - 1))
U1 <- (F1U -1) / (F1U + nr - 1)
F3L <- F31 / qf(1-alpha/2, df21n, df21d)
F3U <- F31 * qf(1-alpha/2, df21d, df21n)
results[1,7] <- L1
results[1,8] <- U1
results[3,7] <- (F3L-1)/(F3L+nr-1)
results[3,8] <- (F3U-1)/(F3U+nr-1)
results[4,7] <- 1- 1/F1L
results[4,8] <- 1- 1/F1U
results[6,7] <- 1- 1/F3L
results[6,8] <- 1 - 1/F3U
#the hard one is case 2
Fj <- MSJ/MSE
vn <- (nr-1)*(ns-1)* ( (nr*ICC2*Fj+ns*(1+(nr-1)*ICC2) - nr*ICC2))^2
vd <- (ns-1)*nr^2 * ICC2^2 * Fj^2 + (ns *(1 + (nr-1)*ICC2) - nr*ICC2)^2
v <- vn/vd
F3U <- qf(1-alpha/2,ns-1,v)
F3L <- qf(1-alpha/2,v,ns-1)
L3 <- ns *(MSB- F3U*MSE)/(F3U*(nr * MSJ + (nr*ns-nr-ns) * MSE)+ ns*MSB)
results[2, 7] <- L3
U3 <- ns *(F3L * MSB - MSE)/(nr * MSJ + (nr * ns - nr - ns)*MSE + ns * F3L * MSB)
results[2, 8] <- U3
L3k <- L3 * nr/(1+ L3*(nr-1))
U3k <- U3 * nr/(1+ U3*(nr-1))
results[5, 7] <- L3k
results[5, 8] <- U3k
#clean up the output
results[,2:8] <- results[,2:8]
type <- match.arg(type, c("all", "ICC1","ICC2","ICC3","ICC1k","ICC2k","ICC3k"))
switch(type
, all={res <- list(results=results, summary=s.aov, stats=stats, MSW=MSW, ns=ns, nr=nr)
class(res) <- "ICC"
}
, ICC1={idx <- 1}
, ICC2={idx <- 2}
, ICC3={idx <- 3}
, ICC1k={idx <- 4}
, ICC2k={idx <- 5}
, ICC3k={idx <- 6}
)
if(type!="all"){
if(is.na(conf.level)){
res <- results[idx, c(2)][,]
} else {
res <- unlist(results[idx, c(2, 7:8)])
names(res) <- c(type,"lwr.ci","upr.ci")
}
}
return(res)
}
print.ICC <- function(x, digits = 3, ...){
cat("\nIntraclass correlation coefficients \n")
print(x$results, digits=digits)
cat("\n Number of subjects =", x$ns, " Number of raters =", x$nr, "\n")
}
# implementing Omega might be wise
# boostrap CI could be integrated in function instead on examples of help
CronbachAlpha <- function(x, conf.level = NA, cond = FALSE, na.rm = FALSE){
i.CronbachAlpha <- function(x, conf.level = NA){
nc <- ncol(x)
colVars <- apply(x, 2, var)
total <- var(rowSums(x))
res <- (total - sum(colVars)) / total * (nc/(nc-1))
if (!is.na(conf.level)) {
N <- length(x)
ci <- 1 - (1-res) * qf( c(1-(1-conf.level)/2, (1-conf.level)/2), N-1, (nc-1)*(N-1))
res <- c("Cronbach Alpha"=res, lwr.ci=ci[1], upr.ci=ci[2])
}
return(res)
}
x <- as.matrix(x)
if(na.rm) x <- na.omit(x)
res <- i.CronbachAlpha(x = x, conf.level = conf.level)
if(cond) {
condCronbachAlpha <- list()
n <- ncol(x)
if(n > 2) { # can't calculate conditional with only 2 items
for(i in 1:n){
condCronbachAlpha[[i]] <- i.CronbachAlpha(x[,-i], conf.level = conf.level)
}
condCronbachAlpha <- data.frame(Item = 1:n, do.call("rbind", condCronbachAlpha))
colnames(condCronbachAlpha)[2] <- "Cronbach Alpha"
}
res <- list(unconditional=res, condCronbachAlpha = condCronbachAlpha)
}
return(res)
}
KendallW <- function(x, correct=FALSE, test=FALSE, na.rm = FALSE) {
# see also old Jim Lemon function kendall.w
# other solution: library(irr); kendall(ratings, correct = TRUE)
# http://www.real-statistics.com/reliability/kendalls-w/
dname <- deparse(substitute(x))
ratings <- as.matrix(x)
if(na.rm) ratings <- na.omit(ratings)
ns <- nrow(ratings)
nr <- ncol(ratings)
#Without correction for ties
if (!correct) {
#Test for ties
TIES = FALSE
testties <- apply(ratings, 2, unique)
if (!is.matrix(testties)) TIES=TRUE
else { if (length(testties) < length(ratings)) TIES=TRUE }
ratings.rank <- apply(ratings,2,rank)
coeff.name <- "W"
coeff <- (12*var(apply(ratings.rank,1,sum))*(ns-1))/(nr^2*(ns^3-ns))
}
else { #With correction for ties
ratings <- as.matrix(na.omit(ratings))
ns <- nrow(ratings)
nr <- ncol(ratings)
ratings.rank <- apply(ratings,2,rank)
Tj <- 0
for (i in 1:nr) {
rater <- table(ratings.rank[,i])
ties <- rater[rater>1]
l <- as.numeric(ties)
Tj <- Tj + sum(l^3-l)
}
coeff.name <- "Wt"
coeff <- (12*var(apply(ratings.rank,1,sum))*(ns-1))/(nr^2*(ns^3-ns)-nr*Tj)
}
if(test){
#test statistics
Xvalue <- nr*(ns-1)*coeff
df1 <- ns-1
names(df1) <- "df"
p.value <- pchisq(Xvalue, df1, lower.tail = FALSE)
method <- paste("Kendall's coefficient of concordance", coeff.name)
alternative <- paste(coeff.name, "is greater 0")
names(ns) <- "subjects"
names(nr) <- "raters"
names(Xvalue) <- "Kendall chi-squared"
names(coeff) <- coeff.name
rval <- list(#subjects = ns, raters = nr,
estimate = coeff, parameter=c(df1, ns, nr),
statistic = Xvalue, p.value = p.value,
alternative = alternative, method = method, data.name = dname)
class(rval) <- "htest"
} else {
rval <- coeff
}
if (!correct && TIES) warning("Coefficient may be incorrect due to ties")
return(rval)
}
CCC <- function(x, y, ci = "z-transform", conf.level = 0.95, na.rm = FALSE){
dat <- data.frame(x, y)
if(na.rm) dat <- na.omit(dat)
# id <- complete.cases(dat)
# nmissing <- sum(!complete.cases(dat))
# dat <- dat[id,]
N. <- 1 - ((1 - conf.level) / 2)
zv <- qnorm(N., mean = 0, sd = 1)
lower <- "lwr.ci"
upper <- "upr.ci"
k <- length(dat$y)
yb <- mean(dat$y)
sy2 <- var(dat$y) * (k - 1) / k
sd1 <- sd(dat$y)
xb <- mean(dat$x)
sx2 <- var(dat$x) * (k - 1) / k
sd2 <- sd(dat$x)
r <- cor(dat$x, dat$y)
sl <- r * sd1 / sd2
sxy <- r * sqrt(sx2 * sy2)
p <- 2 * sxy / (sx2 + sy2 + (yb - xb)^2)
delta <- (dat$x - dat$y)
rmean <- apply(dat, MARGIN = 1, FUN = mean)
blalt <- data.frame(mean = rmean, delta)
# Scale shift:
v <- sd1 / sd2
# Location shift relative to the scale:
u <- (yb - xb) / ((sx2 * sy2)^0.25)
# Variable C.b is a bias correction factor that measures how far the best-fit line deviates from a line at 45 degrees (a measure of accuracy). No deviation from the 45 degree line occurs when C.b = 1. See Lin (1989 page 258).
# C.b <- (((v + 1) / (v + u^2)) / 2)^-1
# The following taken from the Stata code for function "concord" (changed 290408):
C.b <- p / r
# Variance, test, and CI for asymptotic normal approximation (per Lin (March 2000) Biometrics 56:325-5):
sep = sqrt(((1 - ((r)^2)) * (p)^2 * (1 - ((p)^2)) / (r)^2 + (2 * (p)^3 * (1 - p) * (u)^2 / r) - 0.5 * (p)^4 * (u)^4 / (r)^2 ) / (k - 2))
ll = p - zv * sep
ul = p + zv * sep
# Statistic, variance, test, and CI for inverse hyperbolic tangent transform to improve asymptotic normality:
t <- log((1 + p) / (1 - p)) / 2
set = sep / (1 - ((p)^2))
llt = t - zv * set
ult = t + zv * set
llt = (exp(2 * llt) - 1) / (exp(2 * llt) + 1)
ult = (exp(2 * ult) - 1) / (exp(2 * ult) + 1)
if(ci == "asymptotic"){
rho.c <- as.data.frame(cbind(p, ll, ul))
names(rho.c) <- c("est", lower, upper)
rval <- list(rho.c = rho.c, s.shift = v, l.shift = u, C.b = C.b, blalt = blalt ) # , nmissing = nmissing)
}
else if(ci == "z-transform"){
rho.c <- as.data.frame(cbind(p, llt, ult))
names(rho.c) <- c("est", lower, upper)
rval <- list(rho.c = rho.c, s.shift = v, l.shift = u, C.b = C.b, blalt = blalt) #, nmissing = nmissing)
}
return(rval)
}
KrippAlpha <- function (x, method = c("nominal", "ordinal", "interval", "ratio")) {
method <- match.arg(method)
coincidence.matrix <- function(x) {
levx <- (levels(as.factor(x)))
nval <- length(levx)
cm <- matrix(rep(0, nval * nval), nrow = nval)
dimx <- dim(x)
vn <- function(datavec) sum(!is.na(datavec))
if(any(is.na(x))) mc <- apply(x, 2, vn) - 1
else mc <- rep(1, dimx[2])
for(col in 1:dimx[2]) {
for(i1 in 1:(dimx[1] - 1)) {
for(i2 in (i1 + 1):dimx[1]) {
if(!is.na(x[i1, col]) && !is.na(x[i2, col])) {
index1 <- which(levx == x[i1, col])
index2 <- which(levx == x[i2, col])
cm[index1, index2] <- cm[index1,index2] + (1 + (index1 == index2))/mc[col]
if(index1 != index2) cm[index2,index1] <- cm[index1,index2]
}
}
}
}
nmv <- sum(apply(cm, 2, sum))
return(structure(list(method="Krippendorff's alpha",
subjects=dimx[2], raters=dimx[1],irr.name="alpha",
value=NA,stat.name="nil",statistic=NULL,
cm=cm,data.values=levx,nmatchval=nmv,data.level=NA),
class = "irrlist"))
}
ka <- coincidence.matrix(x)
ka$data.level <- method
dimcm <- dim(ka$cm)
utcm <- as.vector(ka$cm[upper.tri(ka$cm)])
diagcm <- diag(ka$cm)
occ <- sum(diagcm)
nc <- apply(ka$cm,1,sum)
ncnc <- sum(nc * (nc - 1))
dv <- as.numeric(ka$data.values)
diff2 <- rep(0,length(utcm))
ncnk <- rep(0,length(utcm))
ck <- 1
if (dimcm[2]<2)
ka$value <- 1.0
else {
for(k in 2:dimcm[2]) {
for(c in 1:(k-1)) {
ncnk[ck] <- nc[c] * nc[k]
if(match(method[1],"nominal",0)) diff2[ck] <- 1
if(match(method[1],"ordinal",0)) {
diff2[ck] <- nc[c]/2
if(k > (c+1))
for(g in (c+1):(k-1)) diff2[ck] <- diff2[ck] + nc[g]
diff2[ck] <- diff2[ck]+nc[k]/2
diff2[ck] <- diff2[ck]^2
}
if(match(method[1],"interval",0)) diff2[ck] <- (dv[c]-dv[k])^2
if(match(method[1],"ratio",0)) diff2[ck] <- (dv[c]-dv[k])^2/(dv[c]+dv[k])^2
ck <- ck+1
}
}
ka$value <- 1-(ka$nmatchval-1)*sum(utcm*diff2)/sum(ncnk*diff2)
}
return(ka)
}
Entropy <- function(x, y = NULL, base = 2, ...) {
# x is either a table or a vector if y is defined
if(!is.null(y)) { x <- table(x, y, ...) }
x <- as.matrix(x)
ptab <- x / sum(x)
H <- - sum( ifelse(ptab > 0, ptab * log(ptab, base=base), 0) )
return(H)
}
MutInf <- function(x, y = NULL, base = 2, ...){
# ### Ref.: http://en.wikipedia.org/wiki/Cluster_labeling
if(!is.null(y)) { x <- table(x, y, ...) }
x <- as.matrix(x)
return(
Entropy(rowSums(x), base=base) +
Entropy(colSums(x), base=base) - Entropy(x, base=base)
)
}
# Rao's Diversity from ade4 divc
# author:
DivCoef <- function(df, dis = NULL, scale = FALSE){
# checking of user's data and initialization.
if (!inherits(df, "data.frame")) stop("Non convenient df")
if (any(df < 0)) stop("Negative value in df")
if (!is.null(dis)) {
if (!inherits(dis, "dist")) stop("Object of class 'dist' expected for distance")
if (!IsEuclid(dis)) warning("Euclidean property is expected for distance")
dis <- as.matrix(dis)
if (nrow(df)!= nrow(dis)) stop("Non convenient df")
dis <- as.dist(dis)
}
if (is.null(dis)) dis <- as.dist((matrix(1, nrow(df), nrow(df))
- diag(rep(1, nrow(df)))) * sqrt(2))
div <- as.data.frame(rep(0, ncol(df)))
names(div) <- "diversity"
rownames(div) <- names(df)
for (i in 1:ncol(df)) {
if(sum(df[, i]) < 1e-16) div[i, ] <- 0
else div[i, ] <- (t(df[, i]) %*% (as.matrix(dis)^2) %*% df[, i]) / 2 / (sum(df[, i])^2)
}
if(scale == TRUE){
divmax <- DivCoefMax(dis)$value
div <- div / divmax
}
return(div)
}
IsEuclid <- function (distmat, plot = FALSE, print = FALSE, tol = 1e-07) {
"bicenter.wt" <- function (X, row.wt = rep(1, nrow(X)), col.wt = rep(1, ncol(X))) {
X <- as.matrix(X)
n <- nrow(X)
p <- ncol(X)
if (length(row.wt) != n)
stop("length of row.wt must equal the number of rows in x")
if (any(row.wt < 0) || (sr <- sum(row.wt)) == 0)
stop("weights must be non-negative and not all zero")
row.wt <- row.wt/sr
if (length(col.wt) != p)
stop("length of col.wt must equal the number of columns in x")
if (any(col.wt < 0) || (st <- sum(col.wt)) == 0)
stop("weights must be non-negative and not all zero")
col.wt <- col.wt/st
row.mean <- apply(row.wt * X, 2, sum)
col.mean <- apply(col.wt * t(X), 2, sum)
col.mean <- col.mean - sum(row.mean * col.wt)
X <- sweep(X, 2, row.mean)
X <- t(sweep(t(X), 2, col.mean))
return(X)
}
if (!inherits(distmat, "dist"))
stop("Object of class 'dist' expected")
if(any(distmat<tol))
warning("Zero distance(s)")
distmat <- as.matrix(distmat)
n <- ncol(distmat)
delta <- -0.5 * bicenter.wt(distmat * distmat)
lambda <- eigen(delta, symmetric = TRUE, only.values = TRUE)$values
w0 <- lambda[n]/lambda[1]
if (plot)
barplot(lambda)
if (print)
print(lambda)
return((w0 > -tol))
}
DivCoefMax <- function(dis, epsilon = 1e-008, comment = FALSE) {
# inititalisation
if(!inherits(dis, "dist")) stop("Distance matrix expected")
if(epsilon <= 0) stop("epsilon must be positive")
if(!IsEuclid(dis)) stop("Euclidean property is expected for dis")
D2 <- as.matrix(dis)^2 / 2
n <- dim(D2)[1]
result <- data.frame(matrix(0, n, 4))
names(result) <- c("sim", "pro", "met", "num")
relax <- 0 # determination de la valeur initiale x0
x0 <- apply(D2, 1, sum) / sum(D2)
result$sim <- x0 # ponderation simple
objective0 <- t(x0) %*% D2 %*% x0
if (comment == TRUE)
print("evolution of the objective function:")
xk <- x0 # grande boucle de test des conditions de Kuhn-Tucker
repeat {
# boucle de test de nullite du gradient projete
repeat {
maxi.temp <- t(xk) %*% D2 %*% xk
if(comment == TRUE) print(as.character(maxi.temp))
#calcul du gradient
deltaf <- (-2 * D2 %*% xk)
# determination des contraintes saturees
sature <- (abs(xk) < epsilon)
if(relax != 0) {
sature[relax] <- FALSE
relax <- 0
}
# construction du gradient projete
yk <- ( - deltaf)
yk[sature] <- 0
yk[!(sature)] <- yk[!(sature)] - mean(yk[!(
sature)])
# test de la nullite du gradient projete
if (max(abs(yk)) < epsilon) {
break
}
# determination du pas le plus grand compatible avec les contraintes
alpha.max <- as.vector(min( - xk[yk < 0] / yk[yk <
0]))
alpha.opt <- as.vector( - (t(xk) %*% D2 %*% yk) / (
t(yk) %*% D2 %*% yk))
if ((alpha.opt > alpha.max) | (alpha.opt < 0)) {
alpha <- alpha.max
}
else {
alpha <- alpha.opt
}
if (abs(maxi.temp - t(xk + alpha * yk) %*% D2 %*% (
xk + alpha * yk)) < epsilon) {
break
}
xk <- xk + alpha * yk
}
# verification des conditions de KT
if (prod(!sature) == 1) {
if (comment == TRUE)
print("KT")
break
}
vectD2 <- D2 %*% xk
u <- 2 * (mean(vectD2[!sature]) - vectD2[sature])
if (min(u) >= 0) {
if (comment == TRUE)
print("KT")
break
}
else {
if (comment == TRUE)
print("relaxation")
satu <- (1:n)[sature]
relax <- satu[u == min(u)]
relax <-relax[1]
}
}
if (comment == TRUE)
print(list(objective.init = objective0, objective.final
= maxi.temp))
result$num <- as.vector(xk, mode = "numeric")
result$num[result$num < epsilon] <- 0
# ponderation numerique
xk <- x0 / sqrt(sum(x0 * x0))
repeat {
yk <- D2 %*% xk
yk <- yk / sqrt(sum(yk * yk))
if (max(xk - yk) > epsilon) {
xk <- yk
}
else break
}
x0 <- as.vector(yk, mode = "numeric")
result$pro <- x0 / sum(x0) # ponderation propre
result$met <- x0 * x0 # ponderation propre
restot <- list()
restot$value <- DivCoef(cbind.data.frame(result$num), dis)[,1]
restot$vectors <- result
return(restot)
}
# http://sph.bu.edu/otlt/MPH-Modules/BS/BS704_Confidence_Intervals/BS704_Confidence_Intervals8.html
# sas: http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_surveyfreq_a0000000227.htm
# discussion: http://tolstoy.newcastle.edu.au/R/e2/help/06/11/4982.html
#
# RelRisk0 <- function(x, conf.level = NA) {
#
# rr <- (x[1,1]/sum(x[,1])) / (x[1,2]/sum(x[,2]))
# if (is.na(conf.level)) {
# res <- rr
# } else {
# sigma <- x[1,2]/(x[1,1]*sum(x[1,])) + x[2,2]/(x[2,1]*sum(x[2,]))
# qn <- qnorm(1-(1-conf.level)/2)
# ci <- exp(log(rr) + c(-1,1)*qn*sqrt(sigma))
# res <- c("rel. risk"=rr, lwr.ci=ci[1], upr.ci=ci[2])
# }
# return(res)
# }
RelRisk <- function(x, y = NULL, conf.level = NA, method = c("score", "wald", "use.or"), delta = 0.5, ...) {
if(!is.null(y)) x <- table(x, y, ...)
p <- (d <- dim(x))[1L]
if(!is.numeric(x) || length(d) != 2L || p != d[2L] || p !=2L)
stop("'x' is not a 2x2 numeric matrix")
x1 <- x[1,1]
x2 <- x[2,1]
n1 <- x[1,1] + x[1,2]
n2 <- x[2,1] + x[2,2]
rr <- (x[1,1]/sum(x[1,])) / (x[2,1]/sum(x[2,]))
if( !is.na(conf.level)) {
switch( match.arg( arg = method, choices = c("score", "wald", "use.or") )
, "score" = {
# source:
# Agresti-Code: http://www.stat.ufl.edu/~aa/cda/R/two-sample/R2/
# R Code for large-sample score confidence interval for a relative risk
# in a 2x2 table (Koopman 1984, Miettinen and Nurminen 1985, Nurminen 1986).
z = abs(qnorm((1-conf.level)/2))
if ((x2==0) &&(x1==0)){
ul = Inf
ll = 0
}
else{
a1 = n2*(n2*(n2+n1)*x1+n1*(n2+x1)*(z^2))
a2 = -n2*(n2*n1*(x2+x1)+2*(n2+n1)*x2*x1+n1*(n2+x2+2*x1)*(z^2))
a3 = 2*n2*n1*x2*(x2+x1)+(n2+n1)*(x2^2)*x1+n2*n1*(x2+x1)*(z^2)
a4 = -n1*(x2^2)*(x2+x1)
b1 = a2/a1
b2 = a3/a1
b3 = a4/a1
c1 = b2-(b1^2)/3
c2 = b3-b1*b2/3+2*(b1^3)/27
ceta = acos(sqrt(27)*c2/(2*c1*sqrt(-c1)))
t1 = -2*sqrt(-c1/3)*cos(pi/3-ceta/3)
t2 = -2*sqrt(-c1/3)*cos(pi/3+ceta/3)
t3 = 2*sqrt(-c1/3)*cos(ceta/3)
p01 = t1-b1/3
p02 = t2-b1/3
p03 = t3-b1/3
p0sum = p01+p02+p03
p0up = min(p01,p02,p03)
p0low = p0sum-p0up-max(p01,p02,p03)
if( (x2==0) && (x1!=0) ){
ll = (1-(n1-x1)*(1-p0low)/(x2+n1-(n2+n1)*p0low))/p0low
ul = Inf
}
else if( (x2!=n2) && (x1==0)){
ul = (1-(n1-x1)*(1-p0up)/(x2+n1-(n2+n1)*p0up))/p0up
ll = 0
}
else if( (x2==n2) && (x1==n1)){
ul = (n2+z^2)/n2
ll = n1/(n1+z^2)
}
else if( (x1==n1) || (x2==n2) ){
if((x2==n2) && (x1==0)) { ll = 0 }
if((x2==n2) && (x1!=0)) {
phat1 = x2/n2
phat2 = x1/n1
phihat = phat2/phat1
phil = 0.95*phihat
chi2 = 0
while (chi2 <= z){
a = (n2+n1)*phil
b = -((x2+n1)*phil+x1+n2)
c = x2+x1
p1hat = (-b-sqrt(b^2-4*a*c))/(2*a)
p2hat = p1hat*phil
q2hat = 1-p2hat
var = (n2*n1*p2hat)/(n1*(phil-p2hat)+n2*q2hat)
chi2 = ((x1-n1*p2hat)/q2hat)/sqrt(var)
ll = phil
phil = ll/1.0001}}
i = x2
j = x1
ni = n2
nj = n1
if( x1==n1 ){
i = x1
j = x2
ni = n1
nj = n2
}
phat1 = i/ni
phat2 = j/nj
phihat = phat2/phat1
phiu = 1.1*phihat
if((x2==n2) && (x1==0)) {
if(n2<100) {phiu = .01}
else {phiu=0.001}
}
chi1 = 0
while (chi1 >= -z){
a = (ni+nj)*phiu
b = -((i+nj)*phiu+j+ni)
c = i+j
p1hat = (-b-sqrt(b^2-4*a*c))/(2*a)
p2hat = p1hat*phiu
q2hat = 1-p2hat
var = (ni*nj*p2hat)/(nj*(phiu-p2hat)+ni*q2hat)
chi1 = ((j-nj*p2hat)/q2hat)/sqrt(var)
phiu1 = phiu
phiu = 1.0001*phiu1
}
if(x1==n1) {
ul = (1-(n1-x1)*(1-p0up)/(x2+n1-(n2+n1)*p0up))/p0up
ll = 1/phiu1
}
else{ ul = phiu1}
}
else{
ul = (1-(n1-x1)*(1-p0up)/(x2+n1-(n2+n1)*p0up))/p0up
ll = (1-(n1-x1)*(1-p0low)/(x2+n1-(n2+n1)*p0low))/p0low
}
}
}
, "wald" = {
# based on code by Michael Dewey, 2006
x1.d <- x1 + delta
x2.d <- x2 + delta
lrr <- log(rr)
se.lrr <- sqrt(1/x1.d - 1/n1 + 1/x2.d - 1/n2)
mult <- abs(qnorm((1-conf.level)/2))
ll <- exp(lrr - mult * se.lrr)
ul <- exp(lrr + mult * se.lrr)
}
, "use.or" = {
or <- OddsRatio(x, conf.level=conf.level)
p2 <- x2/n2
rr.ci <- or/((1-p2) + p2 * or)
ll <- unname(rr.ci[2])
ul <- unname(rr.ci[3])
}
)
}
if (is.na(conf.level)) {
res <- rr
} else {
res <- c("rel. risk"=rr, lwr.ci=ll, upr.ci=ul)
}
return(res)
}
OddsRatio <- function (x, conf.level = NULL, ...) {
UseMethod("OddsRatio")
}
OddsRatio.glm <- function(x, conf.level = NULL, digits=3, use.profile=FALSE, ...) {
if(is.null(conf.level)) conf.level <- 0.95
# Fasst die Ergebnisse eines binomialen GLMs als OR summary zusammen
d.res <- data.frame(summary(x)$coefficients)
names(d.res)[c(2,4)] <- c("Std. Error","Pr(>|z|)")
d.res$or <- exp(d.res$Estimate)
# ci or
d.res$"or.lci" <- exp(d.res$Estimate + qnorm(0.025)*d.res$"Std. Error" )
d.res$"or.uci" <- exp(d.res$Estimate + qnorm(0.975)*d.res$"Std. Error" )
if(use.profile)
ci <- exp(confint(x, level = conf.level))
else
ci <- exp(confint.default(x, level = conf.level))
# exclude na coefficients here, as summary does not yield those
d.res[, c("or.lci","or.uci")] <- ci[!is.na(coefficients(x)), ]
d.res$sig <- Format(d.res$"Pr(>|z|)", fmt="*")
d.res$pval <- Format(d.res$"Pr(>|z|)", fmt="p")
# d.res["(Intercept)",c("or","or.lci","or.uci")] <- NA
# d.res["(Intercept)","Pr(>|z|)"] <- "NA"
# d.res["(Intercept)"," "] <- ""
d.print <- data.frame(lapply(d.res[, 5:7], Format, digits=digits),
pval=d.res$pval, sig=d.res$sig, stringsAsFactors = FALSE)
rownames(d.print) <- rownames(d.res)
colnames(d.print)[4:5] <- c("Pr(>|z|)","")
mterms <- {
res <- lapply(labels(terms(x)), function(y)
colnames(model.matrix(formula(gettextf("~ 0 + %s", y)), data=model.frame(x))))
names(res) <- labels(terms(x))
res
}
res <- list(or=d.print, call=x$call,
BrierScore=BrierScore(x), PseudoR2=PseudoR2(x, which="all"), res=d.res,
nobs=nobs(x), terms=mterms, model=x$model)
class(res) <- "OddsRatio"
return(res)
}
OddsRatio.multinom <- function(x, conf.level=NULL, digits=3, ...) {
if(is.null(conf.level)) conf.level <- 0.95
# class(x) <- class(x)[class(x)!="regr"]
r.summary <- summary(x, Wald.ratios = TRUE)
coe <- t(r.summary$coefficients)
coe <- reshape(data.frame(coe, id=row.names(coe)), varying=1:ncol(coe), idvar="id"
, times=colnames(coe), v.names="or", direction="long")
se <- t(r.summary$standard.errors)
se <- reshape(data.frame(se), varying=1:ncol(se),
times=colnames(se), v.names="se", direction="long")[, "se"]
# d.res <- r.summary
d.res <- data.frame(
"or"= exp(coe[, "or"]),
"or.lci" = exp(coe[, "or"] + qnorm(0.025) * se),
"or.uci" = exp(coe[, "or"] - qnorm(0.025) * se),
"pval" = 2*(1-pnorm(q = abs(coe[, "or"]/se), mean=0, sd=1)),
"sig" = 2*(1-pnorm(q = abs(coe[, "or"]/se), mean=0, sd=1))
)
d.print <- data.frame(
"or"= Format(exp(coe[, "or"]), digits=digits),
"or.lci" = Format(exp(coe[, "or"] + qnorm(0.025) * se), digits=digits),
"or.uci" = Format(exp(coe[, "or"] - qnorm(0.025) * se), digits=digits),
"pval" = Format(2*(1-pnorm(q = abs(coe[, "or"]/se), mean=0, sd=1)), fmt="p", digits=3),
"sig" = Format(2*(1-pnorm(q = abs(coe[, "or"]/se), mean=0, sd=1)), fmt="*"),
stringsAsFactors = FALSE
)
colnames(d.print)[4:5] <- c("Pr(>|z|)","")
rownames(d.print) <- paste(coe$time, coe$id, sep=":")
rownames(d.res) <- rownames(d.print)
res <- list(or = d.print, call = x$call,
BrierScore = NA, # BrierScore(x),
PseudoR2 = PseudoR2(x, which="all"), res=d.res)
class(res) <- "OddsRatio"
return(res)
}
OddsRatio.zeroinfl <- function (x, conf.level = NULL, digits = 3, ...) {
if(is.null(conf.level)) conf.level <- 0.95
d.res <- data.frame(summary(x)$coefficients$zero)
names(d.res)[c(2, 4)] <- c("Std. Error", "Pr(>|z|)")
d.res$or <- exp(d.res$Estimate)
d.res$or.lci <- exp(d.res$Estimate + qnorm(0.025) * d.res$"Std. Error")
d.res$or.uci <- exp(d.res$Estimate + qnorm(0.975) * d.res$"Std. Error")
d.res["(Intercept)", c("or", "or.lci", "or.uci")] <- NA
d.res$sig <- format(as.character(cut(d.res$"Pr(>|z|)", breaks = c(0,
0.001, 0.01, 0.05, 0.1, 1), include.lowest = TRUE, labels = c("***",
"**", "*", ".", " "))), justify = "left")
d.res$"Pr(>|z|)" <- Format(d.res$"Pr(>|z|)", fmt="p")
d.res["(Intercept)", "Pr(>|z|)"] <- "NA"
d.res["(Intercept)", " "] <- ""
d.print <- data.frame(lapply(d.res[, 5:7], Format, digits=digits),
p.value = d.res[,4], sig = d.res[, 8], stringsAsFactors = FALSE)
rownames(d.print) <- rownames(d.res)
res <- list(or = d.print, call = x$call,
BrierScore = BrierScore(resp=(model.response(model.frame(x)) > 0) * 1L,
pred=predict(x, type="zero")),
PseudoR2 = PseudoR2(x, which="all"), res=d.res)
class(res) <- "OddsRatio"
return(res)
}
print.OddsRatio <- function(x, ...){
cat("\nCall:\n")
print(x$call)
cat("\nOdds Ratios:\n")
print(x$or)
cat("---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n\n")
if(!is.null(x$BrierScore)){
cat(gettextf("Brier Score: %s Nagelkerke R2: %s\n\n",
round(x$BrierScore,3), round(x$PseudoR2["Nagelkerke"],3)))
}
}
plot.OddsRatio <- function(x, intercept=FALSE, group=NULL, subset = NULL, ...){
if(!intercept)
# x$res <- x$res[rownames(x$res)!="(Intercept)", ]
x$res <- x$res[!grepl("(Intercept)", rownames(x$res)), ]
args <- list(...)
# here the defaults
args.errbars1 <- list(from=cbind(x$res$or, x$res$or.lci, x$res$or.uci))
# overwrite with userdefined values
if (!is.null(args[["args.errbars"]])) {
args.errbars1[names(args[["args.errbars"]])] <- args[["args.errbars"]][]
args[["args.errbars"]] <- NULL
}
# here the defaults for PlotDot
args.plotdot1 <- list(x=x$res$or, args.errbars=args.errbars1, labels=rownames(x$res),
panel.first=quote(abline(v=1, col="grey")))
if (!is.null(args)) {
args.plotdot1[names(args)] <- args
}
do.call(PlotDot, args=args.plotdot1)
}
OddsRatio.default <- function(x, conf.level = NULL, y = NULL, method=c("wald", "mle", "midp")
, interval = c(0, 1000), ...) {
if(!is.null(y)) x <- table(x, y, ...)
if(is.null(conf.level)) conf.level <- NA
p <- (d <- dim(x))[1L]
if(!is.numeric(x) || length(d) != 2L || p != d[2L] || p != 2L)
stop("'x' is not a 2x2 numeric matrix")
switch( match.arg( arg = method, choices = c("wald", "mle", "midp") )
, "wald" = {
if (any(x == 0)) x <- x + 0.5
lx <- log(x)
or <- exp(lx[1, 1] + lx[2, 2] - lx[1, 2] - lx[2, 1])
if(is.na(conf.level)){
res <- or
} else {
# Agresti Categorical Data Analysis, 3.1.1
sigma2lor <- sum(1/x)
ci <- or * exp(c(1,-1) * qnorm((1-conf.level)/2) * sqrt(sigma2lor))
res <- c("odds ratio"=or, lwr.ci=ci[1], upr.ci=ci[2])
}
}
, "mle" = {
if(is.na(conf.level)){
res <- unname(fisher.test(x, conf.int=FALSE)$estimate)
} else {
res <- fisher.test(x, conf.level=conf.level)
res <- c(res$estimate, lwr.ci=res$conf.int[1], upr.ci=res$conf.int[2])
}
}
, "midp" = {
# based on code from Tomas J. Aragon Developer <aragon at berkeley.edu>
a1 <- x[1,1]; a0 <- x[1,2]; b1 <- x[2,1]; b0 <- x[2,2]; or <- 1
# median-unbiased estimate function
mue <- function(a1, a0, b1, b0, or){
mm <- matrix(c(a1,a0,b1,b0), 2, 2, byrow=TRUE)
fisher.test(mm, or=or, alternative="l")$p-fisher.test(x=x, or=or, alternative="g")$p
}
##mid-p function
midp <- function(a1, a0, b1, b0, or = 1){
mm <- matrix(c(a1,a0,b1,b0),2,2, byrow=TRUE)
lteqtoa1 <- fisher.test(mm,or=or,alternative="l")$p.val
gteqtoa1 <- fisher.test(mm,or=or,alternative="g")$p.val
0.5*(lteqtoa1-gteqtoa1+1)
}
# root finding
EST <- uniroot(
function(or){ mue(a1, a0, b1, b0, or)},
interval = interval)$root
if(is.na(conf.level)){
res <- EST
} else {
alpha <- 1 - conf.level
LCL <- uniroot(function(or){
1-midp(a1, a0, b1, b0, or)-alpha/2
}, interval = interval)$root
UCL <- 1/uniroot(function(or){
midp(a1, a0, b1, b0, or=1/or)-alpha/2
}, interval = interval)$root
res <- c("odds ratio" = EST, lwr.ci=LCL, upr.ci= UCL)
}
}
)
return(res)
}
## odds ratio (OR) to relative risk (RR)
ORToRelRisk <- function(...) {
UseMethod("ORToRelRisk")
}
ORToRelRisk.default <- function(or, p0, ...) {
if(any(or <= 0))
stop("'or' has to be positive")
if(!all(ZeroIfNA(p0) %[]% c(0,1)))
stop("'p0' has to be in (0,1)")
or / (1 - p0 + p0*or)
}
ORToRelRisk.OddsRatio <- function(x, ...){
.PredPrevalence <- function(model) {
isNumericPredictor <- function(model, term){
unname(attr(attr(model, "terms"), "dataClasses")[term] == "numeric")
}
# mean of response ist used for all numeric predictors
meanresp <- mean(as.numeric(model.response(model)) - 1)
# this is ok, as the second level is the one we predict in glm
# https://stackoverflow.com/questions/23282048/logistic-regression-defining-reference-level-in-r
preds <- attr(terms(model), "term.labels")
# first the intercept
res <- NA_real_
for(i in seq_along(preds))
if(isNumericPredictor(model=model, term=preds[i]))
res <- c(res, meanresp)
else {
# get the proportions of the levels of the factor with the response ...
fprev <- prop.table(table(model.frame(model)[, preds[i]],
model.response(model)), 1)
# .. and use the proportion of positive response of the reference level
res <- c(res, rep(fprev[1, 2], times=nrow(fprev)-1))
}
return(res)
}
or <- x$res[, c("or", "or.lci", "or.uci")]
pprev <- .PredPrevalence(x$model)
res <- sapply(or, function(x) ORToRelRisk(x, pprev))
rownames(res) <- rownames(or)
colnames(res) <- c("rr", "rr.lci", "rr.uci")
return(res)
}
# Cohen, Jacob. 1988. Statistical power analysis for the behavioral
# sciences, (2nd edition). Lawrence Erlbaum Associates, Hillsdale, New
# Jersey, United States.
# Garson, G. David. 2007. Statnotes: Topics in Multivariate
# Analysis. URL:
# http://www2.chass.ncsu.edu/garson/pa765/statnote.htm. Visited Spring
# 2006 -- Summer 2007.
# Goodman, Leo A. and William H. Kruskal. 1954. Measures of Association
# for Cross-Classifications. Journal of the American Statistical
# Association, Vol. 49, No. 268 (December 1954), pp. 732-764.
# Liebetrau, Albert M. 1983. Measures of Association. Sage University
# Paper series on Quantitative Applications in the Social Sciences,
# 07-032. Sage Publications, Beverly Hills and London, United
# States/England.
# Margolin, Barry H. and Richard J. Light. 1974. An Analysis of Variance
# for Categorical Data II: Small Sample Comparisons with Chi Square and
# Other Competitors. Journal of the American Statistical Association,
# Vol. 69, No. 347 (September 1974), pp. 755-764.
# Reynolds, H. T. 1977. Analysis of Nominal Data. Sage University Paper
# series on Quantitative Applications in the Social Sciences, 08-007,
# Sage Publications, Beverly Hills/London, California/UK.
# SAS Institute. 2007. Measures of Association
# http://support.sas.com/onlinedoc/913/getDoc/en/statug.hlp/freq_sect20.htm
# Visited January 2007.
# Theil, Henri. 1970. On the Estimation of Relationships Involving
# Qualitative Variables. The American Journal of Sociology, Vol. 76,
# No. 1 (July 1970), pp. 103-154.
# N.B. One should use the values for the significance of the
# Goodman-Kruskal lambda and Theil's UC with reservation, as these
# have been modeled to mimic the behavior of the same statistics
# in SPSS.
GoodmanKruskalTau <- function(x, y = NULL, direction = c("row", "column"), conf.level = NA, ...){
if(!is.null(y)) x <- table(x, y, ...)
n <- sum(x)
n.err.unconditional <- n^2
sum.row <- rowSums(x)
sum.col <- colSums(x)
switch( match.arg( arg = direction, choices = c("row", "column") )
, "column" = { # Tau Column|Row
for(i in 1:nrow(x))
n.err.unconditional <- n.err.unconditional-n*sum(x[i,]^2/sum.row[i])
n.err.conditional <- n^2-sum(sum.col^2)
tau.CR <- 1-(n.err.unconditional/n.err.conditional)
v <- n.err.unconditional/(n^2)
d <- n.err.conditional/(n^2)
f <- d*(v+1)-2*v
var.tau.CR <- 0
for(i in 1:nrow(x))
for(j in 1:ncol(x))
var.tau.CR <- var.tau.CR + x[i,j]*(-2*v*(sum.col[j]/n)+d*((2*x[i,j]/sum.row[i])-sum((x[i,]/sum.row[i])^2))-f)^2/(n^2*d^4)
ASE.tau.CR <- sqrt(var.tau.CR)
est <- tau.CR
sigma2 <- ASE.tau.CR^2
}
, "row" = { # Tau Row|Column
for(j in 1:ncol(x))
n.err.unconditional <- n.err.unconditional-n*sum(x[,j]^2/sum.col[j])
n.err.conditional <- n^2-sum(sum.row^2)
tau.RC <- 1-(n.err.unconditional/n.err.conditional)
v <- n.err.unconditional/(n^2)
d <- n.err.conditional/(n^2)
f <- d*(v+1)-2*v
var.tau.RC <- 0
for(i in 1:nrow(x))
for(j in 1:ncol(x))
var.tau.RC <- var.tau.RC + x[i,j]*(-2*v*(sum.row[i]/n)+d*((2*x[i,j]/sum.col[j])-sum((x[,j]/sum.col[j])^2))-f)^2/(n^2*d^4)
ASE.tau.RC <- sqrt(var.tau.RC)
est <- tau.RC
sigma2 <- ASE.tau.RC^2
}
)
if(is.na(conf.level)){
res <- est
} else {
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + est
res <- c(tauA=est, lwr.ci=ci[1], upr.ci=ci[2])
}
return(res)
}
# good description
# http://salises.mona.uwi.edu/sa63c/Crosstabs%20Measures%20for%20Nominal%20Data.htm
Lambda <- function(x, y = NULL, direction = c("symmetric", "row", "column"), conf.level = NA, ...){
if(!is.null(y)) x <- table(x, y, ...)
# Guttman'a lambda (1941), resp. Goodman Kruskal's Lambda (1954)
n <- sum(x)
csum <- colSums(x)
rsum <- rowSums(x)
rmax <- apply(x, 1, max)
cmax <- apply(x, 2, max)
max.rsum <- max(rsum)
max.csum <- max(csum)
nr <- nrow(x)
nc <- ncol(x)
switch( match.arg( arg = direction, choices = c("symmetric", "row", "column") )
, "symmetric" = { res <- 0.5*(sum(rmax, cmax) - (max.csum + max.rsum)) / (n - 0.5*(max.csum + max.rsum)) }
, "column" = { res <- (sum(rmax) - max.csum) / (n - max.csum) }
, "row" = { res <- (sum(cmax) - max.rsum) / (n - max.rsum) }
)
if(is.na(conf.level)){
res <- res
} else {
L.col <- matrix(,nc)
L.row <- matrix(,nr)
switch( match.arg( arg = direction, choices = c("symmetric", "row", "column") )
, "symmetric" = {
# How to see:
# http://support.sas.com/documentation/cdl/en/statugfreq/63124/PDF/default/statugfreq.pdf
# pp. 1744
# Author: Nina
l <- which.max(csum)
k <- which.max(rsum)
li <- apply(x,1,which.max)
ki <- apply(x,2,which.max)
w <- 2*n-max.csum-max.rsum
v <- 2*n -sum(rmax,cmax)
xx <- sum(rmax[li==l], cmax[ki==k], rmax[k], cmax[l])
y <- 8*n-w-v-2*xx
t <- rep(NA, length(li))
for (i in 1:length(li)){
t[i] <- (ki[li[i]]==i & li[ki[li[i]]]==li[i])
}
sigma2 <- 1/w^4*(w*v*y-2 *w^2*(n - sum(rmax[t]))-2*v^2*(n-x[k,l]))
}
, "column" = {
L.col.max <- min(which(csum == max.csum))
for(i in 1:nr) {
if(length(which(x[i, intersect(which(x[i,] == max.csum), which(x[i,] == max.rsum))] == n))>0)
L.col[i] <- min(which(x[i, intersect(which(x[i,] == max.csum), which(x[i,] == max.rsum))] == n))
else
if(x[i, L.col.max] == max.csum)
L.col[i] <- L.col.max
else
L.col[i] <- min(which(x[i,] == rmax[i]))
}
sigma2 <- (n-sum(rmax))*(sum(rmax) + max.csum -
2*(sum(rmax[which(L.col == L.col.max)])))/(n-max.csum)^3
}
, "row" = {
L.row.max <- min(which(rsum == max.rsum))
for(i in 1:nc) {
if(length(which(x[intersect(which(x[,i] == max.rsum), which(x[,i] == max.csum)),i] == n))>0)
L.row[i] <- min(which(x[i,intersect(which(x[i,] == max.csum), which(x[i,] == max.rsum))] == n))
else
if(x[L.row.max,i] == max.rsum)
L.row[i] <- L.row.max
else
L.row[i] <- min(which(x[,i] == cmax[i]))
}
sigma2 <- (n-sum(cmax))*(sum(cmax) + max.rsum -
2*(sum(cmax[which(L.row == L.row.max)])))/(n-max.rsum)^3
}
)
pr2 <- 1 - (1 - conf.level)/2
ci <- pmin(1, pmax(0, qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + res))
res <- c(lambda = res, lwr.ci=ci[1], upr.ci=ci[2])
}
return(res)
}
UncertCoef <- function(x, y = NULL, direction = c("symmetric", "row", "column"),
conf.level = NA, p.zero.correction = 1/sum(x)^2, ... ) {
# Theil's UC (1970)
# slightly nudge zero values so that their logarithm can be calculated (cf. Theil 1970: x->0 => xlogx->0)
if(!is.null(y)) x <- table(x, y, ...)
x[x == 0] <- p.zero.correction
n <- sum(x)
rsum <- rowSums(x)
csum <- colSums(x)
hx <- -sum((apply(x, 1, sum) * log(apply(x, 1, sum)/n))/n)
hy <- -sum((apply(x, 2, sum) * log(apply(x, 2, sum)/n))/n)
hxy <- -sum(apply(x, c(1, 2), sum) * log(apply(x, c(1, 2), sum)/n)/n)
switch( match.arg( arg = direction, choices = c("symmetric", "row", "column") )
, "symmetric" = { res <- 2 * (hx + hy - hxy)/(hx + hy) }
, "row" = { res <- (hx + hy - hxy)/hx }
, "column" = { res <- (hx + hy - hxy)/hy }
)
if(!is.na(conf.level)){
var.uc.RC <- var.uc.CR <- 0
for(i in 1:nrow(x))
for(j in 1:ncol(x))
{ var.uc.RC <- var.uc.RC + x[i,j]*(hx*log(x[i,j]/csum[j])+((hy-hxy)*log(rsum[i]/n)))^2/(n^2*hx^4);
var.uc.CR <- var.uc.CR + x[i,j]*(hy*log(x[i,j]/rsum[i])+((hx-hxy)*log(csum[j]/n)))^2/(n^2*hy^4);
}
switch( match.arg( arg = direction, choices = c("symmetric", "row", "column") )
, "symmetric" = {
sigma2 <- 4*sum(x * (hxy * log(rsum %o% csum/n^2) - (hx+hy)*log(x/n))^2 ) /
(n^2*(hx+hy)^4)
}
, "row" = { sigma2 <- var.uc.RC }
, "column" = { sigma2 <- var.uc.CR }
)
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + res
res <- c(uc = res, lwr.ci=max(ci[1], -1), upr.ci=min(ci[2], 1))
}
return(res)
}
TheilU <- function(a, p, type = c(2, 1), na.rm = FALSE){
if(na.rm) {
idx <- complete.cases(a, p)
a <- a[idx]
p <- p[idx]
}
n <- length(a)
if(length(p)!=n) {
warning("a must have same length as p")
res <- NA
} else {
switch( match.arg(as.character(type), c("2", "1"))
, "1" = { res <- sqrt(sum((a-p)^2/n))/(sqrt(sum(a^2)/n) + sqrt(sum(p^2)/n)) }
, "2" = { res <- sqrt(sum((a-p)^2))/(sqrt(sum(a^2))) }
)
}
return(res)
}
#S function SomersDelta
#
# Calculates concordance probability and Somers' Dxy rank correlation
# between a variable X (for which ties are counted) and a binary
# variable Y (having values 0 and 1, for which ties are not counted).
# Uses short cut method based on average ranks in two groups.
#
# Usage:
#
# SomersDelta(x, y, weights)
#
# Returns vector whose elements are C Index, Dxy, n and missing, where
# C Index is the concordance probability and Dxy=2(C Index-.5).
#
# F. Harrell 28 Nov 90 6 Apr 98: added weights
#
# SomersDelta2 <- function(x, y, weights=NULL, normwt=FALSE, na.rm=TRUE) {
#
# wtd.mean <- function(x, weights=NULL, normwt='ignored', na.rm=TRUE)
# {
# if(!length(weights)) return(mean(x, na.rm=na.rm))
# if(na.rm) {
# s <- !is.na(x + weights)
# x <- x[s]
# weights <- weights[s]
# }
#
# sum(weights*x)/sum(weights)
# }
#
# wtd.table <- function(x, weights=NULL, type=c('list','table'),
# normwt=FALSE, na.rm=TRUE)
# {
# type <- match.arg(type)
# if(!length(weights))
# weights <- rep(1, length(x))
#
# isdate <- IsDate(x) ### 31aug02 + next 2
# ax <- attributes(x)
# ax$names <- NULL
# x <- if(is.character(x)) as.category(x)
# else unclass(x)
#
# lev <- levels(x)
# if(na.rm) {
# s <- !is.na(x + weights)
# x <- x[s,drop=FALSE] ### drop is for factor class
# weights <- weights[s]
# }
#
# n <- length(x)
# if(normwt)
# weights <- weights*length(x)/sum(weights)
#
# i <- order(x) ### R does not preserve levels here
# x <- x[i]; weights <- weights[i]
#
# if(any(diff(x)==0)) { ### slightly faster than any(duplicated(xo))
# weights <- tapply(weights, x, sum)
# if(length(lev)) { ### 3apr03
# levused <- lev[sort(unique(x))] ### 7sep02
# ### Next 3 lines 21apr03
# if((length(weights) > length(levused)) &&
# any(is.na(weights)))
# weights <- weights[!is.na(weights)]
#
# if(length(weights) != length(levused))
# stop('program logic error')
#
# names(weights) <- levused ### 10Apr01 length 16May01
# }
#
# if(!length(names(weights)))
# stop('program logic error') ### 16May01
#
# if(type=='table')
# return(weights)
#
# x <- all.is.numeric(names(weights),'vector')
# if(isdate)
# attributes(x) <- c(attributes(x),ax) ### 31aug02
#
# names(weights) <- NULL
# return(list(x=x, sum.of.weights=weights))
# }
#
# xx <- x ### 31aug02
# if(isdate)
# attributes(xx) <- c(attributes(xx),ax)
#
# if(type=='list')
# list(x=if(length(lev))lev[x]
# else xx,
# sum.of.weights=weights)
# else {
# names(weights) <- if(length(lev)) lev[x]
# else xx
# weights
# }
# }
#
#
# wtd.rank <- function(x, weights=NULL, normwt=FALSE, na.rm=TRUE)
# {
# if(!length(weights))
# return(rank(x),na.last=if(na.rm)NA else TRUE)
#
# tab <- wtd.table(x, weights, normwt=normwt, na.rm=na.rm)
#
# freqs <- tab$sum.of.weights
# ### rank of x = ### <= x - .5 (# = x, minus 1)
# r <- cumsum(freqs) - .5*(freqs-1)
# ### Now r gives ranks for all unique x values. Do table look-up
# ### to spread these ranks around for all x values. r is in order of x
# approx(tab$x, r, xout=x)$y
# }
#
#
# if(length(y)!=length(x))stop("y must have same length as x")
# y <- as.integer(y)
# wtpres <- length(weights)
# if(wtpres && (wtpres != length(x)))
# stop('weights must have same length as x')
#
# if(na.rm) {
# miss <- if(wtpres) is.na(x + y + weights)
# else is.na(x + y)
#
# nmiss <- sum(miss)
# if(nmiss>0) {
# miss <- !miss
# x <- x[miss]
# y <- y[miss]
# if(wtpres) weights <- weights[miss]
# }
# }
# else nmiss <- 0
#
# u <- sort(unique(y))
# if(any(! y %in% 0:1)) stop('y must be binary')
#
# if(wtpres) {
# if(normwt)
# weights <- length(x)*weights/sum(weights)
# n <- sum(weights)
# }
# else n <- length(x)
#
# if(n<2) stop("must have >=2 non-missing observations")
#
# n1 <- if(wtpres)sum(weights[y==1]) else sum(y==1)
#
# if(n1==0 || n1==n)
# return(c(C=NA,Dxy=NA,n=n,Missing=nmiss))
#
# mean.rank <- if(wtpres)
# wtd.mean(wtd.rank(x, weights, na.rm=FALSE), weights*y)
# else
# mean(rank(x)[y==1])
#
# c.index <- (mean.rank - (n1+1)/2)/(n-n1)
# dxy <- 2*(c.index-.5)
# r <- c(c.index, dxy, n, nmiss)
# names(r) <- c("C", "Dxy", "n", "Missing")
# r
# }
#
SomersDelta <- function(x, y = NULL, direction=c("row","column"), conf.level = NA, ...) {
if(!is.null(y)) tab <- table(x, y, ...)
else tab <- as.table(x)
# tab is a matrix of counts
x <- ConDisPairs(tab)
# use .DoCount
# if(is.na(conf.level)) {
# d.tab <- as.data.frame.table(tab)
# x <- .DoCount(d.tab[,1], d.tab[,2], d.tab[,3])
# } else {
# x <- ConDisPairs(tab)
# }
m <- min(dim(tab))
n <- sum(tab)
switch( match.arg( arg = direction, choices = c("row","column") )
, "row" = { ni. <- colSums(tab) }
, "column" = { ni. <- rowSums(tab) }
)
wt <- n^2 - sum(ni.^2)
# Asymptotic standard error: sqrt(sigma2)
sigma2 <- 4/wt^4 * (sum(tab * (wt*(x$pi.c - x$pi.d) - 2*(x$C-x$D)*(n-ni.))^2))
# debug: print(sqrt(sigma2))
somers <- (x$C - x$D) / (n * (n-1) /2 - sum(ni. * (ni. - 1) /2 ))
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + somers
if(is.na(conf.level)){
result <- somers
} else {
result <- c(somers = somers, lwr.ci=max(ci[1], -1), upr.ci=min(ci[2], 1))
}
return(result)
}
# Computes rank correlation measures between a variable X and a possibly
# censored variable Y, with event/censoring indicator EVENT
# Rank correlation is extension of Somers' Dxy = 2(Concordance Prob-.5)
# See Harrell et al JAMA 1984(?)
# Set outx=T to exclude ties in X from computations (-> Goodman-Kruskal
# gamma-type rank correlation)
# based on rcorr.cens in Hmisc, https://stat.ethz.ch/pipermail/r-help/2003-March/030837.html
# author Frank Harrell
# GoodmanGammaF <- function(x, y) {
# ### Fortran implementation of Concordant/Discordant, but still O(n^2)
# x <- as.numeric(x)
# y <- as.numeric(y)
# event <- rep(TRUE, length(x))
# if(length(y)!=length(x))
# stop("y must have same length as x")
# outx <- TRUE
# n <- length(x)
# ne <- sum(event)
# z <- .Fortran("cidxcn", x, y, event, length(x), nrel=double(1), nconc=double(1),
# nuncert=double(1),
# c.index=double(1), gamma=double(1), sd=double(1), as.logical(outx)
# )
# r <- c(z$c.index, z$gamma, z$sd, n, ne, z$nrel, z$nconc, z$nuncert)
# names(r) <- c("C Index","Dxy","S.D.","n","uncensored",
# "Relevant Pairs",
# "Concordant","Uncertain")
# unname(r[2])
# }
# GoodmanGamma(as.numeric(d.frm$Var1), as.numeric(d.frm$Var2))
# cor(as.numeric(d.frm$Var1), as.numeric(d.frm$Var2))
GoodmanKruskalGamma <- function(x, y = NULL, conf.level = NA, ...) {
if(!is.null(y)) tab <- table(x, y, ...)
else tab <- as.table(x)
# tab is a matrix of counts
# Based on code of Michael Friendly and Laura Thompson
# Confidence interval calculation and output from Greg Rodd
x <- ConDisPairs(tab)
psi <- 2 * (x$D * x$pi.c - x$C * x$pi.d)/(x$C + x$D)^2
# Asymptotic standard error: sqrt(sigma2)
sigma2 <- sum(tab * psi^2) - sum(tab * psi)^2
gamma <- (x$C - x$D)/(x$C + x$D)
if(is.na(conf.level)){
result <- gamma
} else {
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + gamma
result <- c(gamma = gamma, lwr.ci=max(ci[1], -1), upr.ci=min(ci[2], 1))
}
return(result)
}
# KendallTauB.table <- function(tab, conf.level = NA) {
# http://support.sas.com/documentation/cdl/en/statugfreq/63124/PDF/default/statugfreq.pdf
# pp 1738
# tab is a matrix of counts
# x <- ConDisPairs(tab)
# n <- sum(tab)
# ni. <- apply(tab, 1, sum)
# n.j <- apply(tab, 2, sum)
# wr <- n^2 - sum(ni.^2)
# wc <- n^2 - sum(n.j^2)
# w <- sqrt(wr * wc)
# vij <- ni. * wc + n.j * wr
# dij <- x$pi.c - x$pi.d ### Aij - Dij
# Asymptotic standard error: sqrt(sigma2)
# sigma2 <- 1/w^4 * (sum(tab * (2*w*dij + taub*vij)^2) - n^3 * taub^2 * (wr + wc)^2)
# this is the H0 = 0 variance:
# sigma2 <- 4/(wr * wc) * (sum(tab * (x$pi.c - x$pi.d)^2) - 4*(x$C - x$D)^2/n )
# taub <- 2*(x$C - x$D)/sqrt(wr * wc)
# if(is.na(conf.level)){
# result <- taub
# } else {
# pr2 <- 1 - (1 - conf.level)/2
# ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + taub
# result <- c(taub = taub, lwr.ci=max(ci[1], -1), ups.ci=min(ci[2], 1))
# }
# return(result)
# }
KendallTauA <- function(x, y = NULL, direction = c("row", "column"), conf.level = NA, ...){
if(!is.null(y)) tab <- table(x, y, ...)
else tab <- as.table(x)
x <- ConDisPairs(tab)
n <- sum(tab)
n0 <- n*(n-1)/2
taua <- (x$C - x$D) / n0
# Hollander, Wolfe pp. 415/416
# think we should not consider ties here, so take only the !=0 part
Ci <- as.vector((x$pi.c - x$pi.d) * (tab!=0))
Ci <- Ci[Ci!=0]
C_ <- sum(Ci)/n
sigma2 <- 2/(n*(n-1)) * ((2*(n-2))/(n*(n-1)^2) * sum((Ci - C_)^2) + 1 - taua^2)
if (is.na(conf.level)) {
result <- taua
}
else {
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + taua
result <- c(tau_a = taua, lwr.ci = max(ci[1], -1), upr.ci = min(ci[2], 1))
}
return(result)
}
# KendallTauB <- function(x, y = NULL, conf.level = NA, test=FALSE, alternative = c("two.sided", "less", "greater"), ...){
KendallTauB <- function(x, y = NULL, conf.level = NA, ...){
# Ref: http://www.fs.fed.us/psw/publications/lewis/LewisHMP.pdf
# pp 2-9
#
if (!is.null(y)) {
dname <- paste(deparse(substitute(x)), "and", deparse(substitute(y)))
} else {
dname <- deparse(substitute(x))
}
if(!is.null(y)) tab <- table(x, y, ...)
else tab <- as.table(x)
x <- ConDisPairs(tab)
n <- sum(tab)
n0 <- n*(n-1)/2
ti <- rowSums(tab) # apply(tab, 1, sum)
uj <- colSums(tab) # apply(tab, 2, sum)
n1 <- sum(ti * (ti-1) / 2)
n2 <- sum(uj * (uj-1) / 2)
taub <- (x$C - x$D) / sqrt((n0-n1)*(n0-n2))
pi <- tab / sum(tab)
pdiff <- (x$pi.c - x$pi.d) / sum(tab)
Pdiff <- 2 * (x$C - x$D) / sum(tab)^2
rowsum <- rowSums(pi) # apply(pi, 1, sum)
colsum <- colSums(pi) # apply(pi, 2, sum)
rowmat <- matrix(rep(rowsum, dim(tab)[2]), ncol = dim(tab)[2])
colmat <- matrix(rep(colsum, dim(tab)[1]), nrow = dim(tab)[1], byrow = TRUE)
delta1 <- sqrt(1 - sum(rowsum^2))
delta2 <- sqrt(1 - sum(colsum^2))
# Compute asymptotic standard errors taub
tauphi <- (2 * pdiff + Pdiff * colmat) * delta2 * delta1 + (Pdiff * rowmat * delta2)/delta1
sigma2 <- ((sum(pi * tauphi^2) - sum(pi * tauphi)^2)/(delta1 * delta2)^4) / n
# for very small pi/tauph it's possible that sigma2 gets negative so we cut small negative values here
# example: KendallTauB(table(iris$Species, iris$Species))
if(sigma2 < .Machine$double.eps * 10) sigma2 <- 0
if (is.na(conf.level)) {
result <- taub
}
else {
pr2 <- 1 - (1 - conf.level)/2
ci <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + taub
result <- c(tau_b = taub, lwr.ci = max(ci[1], -1), upr.ci = min(ci[2], 1))
}
# if(test){
#
# alternative <- match.arg(alternative)
#
# zstat <- taub / sqrt(sigma2)
#
# if (alternative == "less") {
# pval <- pnorm(zstat)
# cint <- c(-Inf, zstat + qnorm(conf.level))
# }
# else if (alternative == "greater") {
# pval <- pnorm(zstat, lower.tail = FALSE)
# cint <- c(zstat - qnorm(conf.level), Inf)
# }
# else {
# pval <- 2 * pnorm(-abs(zstat))
# alpha <- 1 - conf.level
# cint <- qnorm(1 - alpha/2)
# cint <- zstat + c(-cint, cint)
# }
#
# RVAL <- list()
# RVAL$p.value <- pval
# RVAL$method <- "Kendall's rank correlation tau"
# RVAL$data.name <- dname
# RVAL$statistic <- x$C - x$D
# names(RVAL$statistic) <- "T"
# RVAL$estimate <- taub
# names(RVAL$estimate) <- "tau-b"
# RVAL$conf.int <- c(max(ci[1], -1), min(ci[2], 1))
# # attr(RVAL$conf.int, "conf.level") = round(attr(ci,"conf.level"), 3)
# class(RVAL) <- "htest"
# return(RVAL)
#
# # rval <- list(statistic = zstat, p.value = pval,
# # parameter = sd_pop,
# # conf.int = cint, estimate = estimate, null.value = mu,
# # alternative = alternative, method = method, data.name = dname)
#
# } else {
return(result)
# }
}
# KendallTauB(x, y, conf.level = 0.95, test=TRUE)
#
# cor.test(x,y, method="kendall")
# tab <- as.table(rbind(c(26,26,23,18,9),c(6,7,9,14,23)))
# KendallTauB(tab, conf.level = 0.95)
# Assocs(tab)
StuartTauC <- function(x, y = NULL, conf.level = NA, ...) {
if(!is.null(y)) tab <- table(x, y, ...)
else tab <- as.table(x)
# Reference:
# http://v8doc.sas.com/sashtml/stat/chap28/sect18.htm
x <- ConDisPairs(tab)
m <- min(dim(tab))
n <- sum(tab)
# Asymptotic standard error: sqrt(sigma2)
sigma2 <- 4 * m^2 / ((m-1)^2 * n^4) * (sum(tab * (x$pi.c - x$pi.d)^2) - 4 * (x$C -x$D)^2/n)
# debug: print(sqrt(sigma2))
# Tau-c = (C - D)*[2m/(n2(m-1))]
tauc <- (x$C - x$D) * 2 * min(dim(tab)) / (sum(tab)^2*(min(dim(tab))-1))
if(is.na(conf.level)){
result <- tauc
} else {
pr2 <- 1 - (1 - conf.level)/2
CI <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + tauc
result <- c(tauc = tauc, lwr.ci=max(CI[1], -1), upr.ci=min(CI[2], 1))
}
return(result)
}
# SpearmanRho <- function(x, y = NULL, use = c("everything", "all.obs", "complete.obs",
# "na.or.complete","pairwise.complete.obs"), conf.level = NA ) {
#
# if(is.null(y)) {
# x <- Untable(x)
# y <- x[,2]
# x <- x[,1]
# }
# # Reference:
# # https://stat.ethz.ch/pipermail/r-help/2006-October/114319.html
# # fisher z transformation for calc SpearmanRho ci :
# # Conover WJ, Practical Nonparametric Statistics (3rd edition). Wiley 1999.
#
# # http://support.sas.com/documentation/cdl/en/statugfreq/63124/PDF/default/statugfreq.pdf
# # pp 1738
#
#
# # n <- sum(tab)
# # ni. <- apply(tab, 1, sum)
# # n.j <- apply(tab, 2, sum)
# # F <- n^3 - sum(ni.^3)
# # G <- n^3 - sum(n.j^3)
# # w <- 1/12*sqrt(F * G)
#
# # ### Asymptotic standard error: sqrt(sigma2)
# # sigma2 <- 1
# # ### debug: print(sqrt(sigma2))
#
# # ### Tau-c = (C - D)*[2m/(n2(m-1))]
# # est <- 1
#
# # if(is.na(conf.level)){
# # result <- tauc
# # } else {
# # pr2 <- 1 - (1 - conf.level)/2
# # CI <- qnorm(pr2) * sqrt(sigma2) * c(-1, 1) + est
# # result <- c(SpearmanRho = est, lwr.ci=max(CI[1], -1), ups.ci=min(CI[2], 1))
# # }
#
# # return(result)
#
#
# # Ref:
# # http://www-01.ibm.com/support/docview.wss?uid=swg21478368
#
# use <- match.arg(use, choices=c("everything", "all.obs", "complete.obs",
# "na.or.complete","pairwise.complete.obs"))
#
# rho <- cor(as.numeric(x), as.numeric(y), method="spearman", use = use)
#
# e_fx <- exp( 2 * ((.5 * log((1+rho) / (1-rho))) - c(1, -1) *
# (abs(qnorm((1 - conf.level)/2))) * (1 / sqrt(sum(complete.cases(x,y)) - 3)) ))
# ci <- (e_fx - 1) / (e_fx + 1)
#
# if (is.na(conf.level)) {
# result <- rho
# } else {
# pr2 <- 1 - (1 - conf.level) / 2
# result <- c(rho = rho, lwr.ci = max(ci[1], -1), upr.ci = min(ci[2], 1))
# }
# return(result)
#
# }
# replaced by DescTools v 0.99.36
# as Untable() is a nogo for tables with high frequencies...
SpearmanRho <- function(x, y = NULL, use = c("everything", "all.obs", "complete.obs",
"na.or.complete","pairwise.complete.obs"), conf.level = NA ) {
if(is.null(y)) {
# implemented following
# https://support.sas.com/documentation/onlinedoc/stat/151/freq.pdf
# S. 3103
# http://support.sas.com/documentation/cdl/en/statugfreq/63124/PDF/default/statugfreq.pdf
# pp 1738
# Old References:
# https://stat.ethz.ch/pipermail/r-help/2006-October/114319.html
# fisher z transformation for calc SpearmanRho ci :
# Conover WJ, Practical Nonparametric Statistics (3rd edition). Wiley 1999.
n <- sum(x)
ni. <- apply(x, 1, sum)
n.j <- apply(x, 2, sum)
ri <- rank(rownames(x))
ci <- rank(colnames(x))
ri <- 1:nrow(x)
ci <- 1:ncol(x)
R1i <- c(sapply(seq_along(ri),
function(i) ifelse(i==1, 0, cumsum(ni.)[i-1]) + ni.[i]/2))
C1i <- c(sapply(seq_along(ci),
function(i) ifelse(i==1, 0, cumsum(n.j)[i-1]) + n.j[i]/2))
Ri <- R1i - n/2
Ci <- C1i - n/2
v <- sum(x * outer(Ri, Ci))
F <- n^3 - sum(ni.^3)
G <- n^3 - sum(n.j^3)
w <- 1/12*sqrt(F * G)
rho <- v/w
} else {
# http://www-01.ibm.com/support/docview.wss?uid=swg21478368
use <- match.arg(use, choices=c("everything", "all.obs", "complete.obs",
"na.or.complete","pairwise.complete.obs"))
rho <- cor(as.numeric(x), as.numeric(y), method="spearman", use = use)
n <- complete.cases(x,y)
}
e_fx <- exp( 2 * ((.5 * log((1+rho) / (1-rho))) - c(1, -1) *
(abs(qnorm((1 - conf.level)/2))) * (1 / sqrt(sum(n) - 3)) ))
ci <- (e_fx - 1) / (e_fx + 1)
if (is.na(conf.level)) {
result <- rho
} else {
if(identical(rho, 1)){ # will blast the fisher z transformation
result <- c(rho=1, lwr.ci=1, upr.ci=1)
} else {
pr2 <- 1 - (1 - conf.level) / 2
result <- c(rho = rho, lwr.ci = max(ci[1], -1), upr.ci = min(ci[2], 1))
}
}
return(result)
}
# Definitions:
# http://v8doc.sas.com/sashtml/stat/chap28/sect18.htm
ConDisPairs <-function(x){
# tab is a matrix of counts
# Based on code of Michael Friendly and Laura Thompson
# slooooow because of 2 nested for clauses O(n^2)
# this is NOT faster when implemented with a mapply(...)
# Lookin for alternatives in C
# http://en.verysource.com/code/1169955_1/kendl2.cpp.html
# cor(..., "kendall") is for dimensions better
n <- nrow(x)
m <- ncol(x)
pi.c <- pi.d <- matrix(0, nrow = n, ncol = m)
row.x <- row(x)
col.x <- col(x)
for(i in 1:n){
for(j in 1:m){
pi.c[i, j] <- sum(x[row.x<i & col.x<j]) + sum(x[row.x>i & col.x>j])
pi.d[i, j] <- sum(x[row.x<i & col.x>j]) + sum(x[row.x>i & col.x<j])
}
}
C <- sum(pi.c * x)/2
D <- sum(pi.d * x)/2
return(list(pi.c = pi.c, pi.d = pi.d, C = C, D = D))
}
BinTree <- function(n) {
ranks <- rep(0L, n)
yet.to.do <- 1:n
depth <- floor(logb(n, 2))
start <- as.integer(2^depth)
lastrow.length <- 1 + n - start
indx <- seq(1L, by = 2L, length = lastrow.length)
ranks[yet.to.do[indx]] <- start + 0:(length(indx) - 1L)
yet.to.do <- yet.to.do[-indx]
while (start > 1) {
start <- as.integer(start/2)
indx <- seq(1L, by = 2L, length = start)
ranks[yet.to.do[indx]] <- start + 0:(start - 1L)
yet.to.do <- yet.to.do[-indx]
}
return(ranks)
}
PlotBinTree <- function(x, main="Binary tree", horiz=FALSE, cex=1.0, col=1, ...){
bimean <- function(x){
(x[rep(c(TRUE, FALSE), length.out=length(x))] +
x[rep(c(FALSE, TRUE), length.out=length(x))]) / 2
}
n <- length(x)
s <- floor(log(n, 2))
# if(sortx)
# x <- sort(x)
# else
# x <- x[BinTree(length(x))]
lst <- list()
lst[[s+1]] <- 1:2^s
for(i in s:1){
lst[[i]] <- bimean(lst[[i+1]])
}
d.frm <- merge(
x=data.frame(x=x, binpos=BinTree(length(x))),
y=data.frame(xpos = unlist(lst),
ypos = -rep(1:length(lst), unlist(lapply(lst, length))),
pos = 1:(2^(s+1)-1)
), by.x="binpos", by.y="pos")
if(horiz){
Canvas(xlim=c(1, s+1.5), ylim=c(0, 2^s+1), main=main,
asp=FALSE, mar=c(0,0,2,0)+1 )
ii <- 0
for(i in 1L:(length(lst)-1)){
for(j in seq_along(lst[[i]])){
ii <- ii + 1
if(ii < n)
segments(y0=lst[[i]][j], x0=i, y1=lst[[i+1]][2*(j-1)+1], x1=i+1, col=col)
ii <- ii + 1
if(ii < n)
segments(y0=lst[[i]][j], x0=i, y1=lst[[i+1]][2*(j-1)+2], x1=i+1, col=col)
}
}
# Rotate positions for the text
# rotxy <- Rotate(d.frm$xpos, d.frm$ypos, theta=pi/2)
# d.frm$xpos <- rotxy$x
# d.frm$ypos <- rotxy$y
m <- d.frm$xpos
d.frm$xpos <- -d.frm$ypos
d.frm$ypos <- m
} else {
Canvas(xlim=c(0,2^s+1), ylim=c(-s,1)-1.5, main=main,
asp=FALSE, mar=c(0,0,2,0)+1, ...)
ii <- 0
for(i in 1L:(length(lst)-1)){
for(j in seq_along(lst[[i]])){
ii <- ii + 1
if(ii < n)
segments(x0=lst[[i]][j], y0=-i, x1=lst[[i+1]][2*(j-1)+1], y1=-i-1, col=col)
ii <- ii + 1
if(ii < n)
segments(x0=lst[[i]][j], y0=-i, x1=lst[[i+1]][2*(j-1)+2], y1=-i-1, col=col)
}
}
}
BoxedText(x=d.frm$xpos, y=d.frm$ypos, labels=d.frm$x, cex=cex,
border=NA, xpad = 0.5, ypad = 0.5)
invisible(d.frm)
}
.DoCount <- function(y, x, wts) {
# O(n log n):
# http://www.listserv.uga.edu/cgi-bin/wa?A2=ind0506d&L=sas-l&P=30503
if(missing(wts)) wts <- rep_len(1L, length(x))
ord <- order(y)
ux <- sort(unique(x))
n2 <- length(ux)
idx <- BinTree(n2)[match(x[ord], ux)] - 1L
y <- cbind(y,1)
res <- .Call("conc", PACKAGE="DescTools", y[ord,], as.double(wts[ord]),
as.integer(idx), as.integer(n2))
return(list(pi.c = NA, pi.d = NA, C = res[2], D = res[1], T=res[3], N=res[4]))
}
.assocs_condis <- function(x, y = NULL, conf.level = NA, ...) {
# (very) fast function for calculating all concordant/discordant pairs based measures
# all table operations are cheap compared to the counting of cons/disc...
# no implementation for confidence levels so far.
if(!is.null(y))
x <- table(x, y)
# we need rowsums and colsums, so tabling is mandatory...
# use weights
x <- as.table(x)
min_dim <- min(dim(x))
n <- sum(x)
ni. <- rowSums(x)
nj. <- colSums(x)
n0 <- n*(n-1L)/2
n1 <- sum(ni. * (ni.-1L) / 2)
n2 <- sum(nj. * (nj.-1L) / 2)
x <- as.data.frame(x)
z <- .DoCount(x[,1], x[,2], x[,3])
gamma <- (z$C - z$D)/(z$C + z$D)
somers_r <- (z$C - z$D) / (n0 - n2)
somers_c <- (z$C - z$D) / (n0 - n1)
taua <- (z$C - z$D) / n0
taub <- (z$C - z$D) / sqrt((n0-n1)*(n0-n2))
tauc <- (z$C - z$D) * 2 * min_dim / (n^2*(min_dim-1L))
if(is.na(conf.level)){
result <- c(gamma=gamma, somers_r=somers_r, somers_c=somers_c,
taua=taua, taub=taub, tauc=tauc)
} else {
# psi <- 2 * (x$D * x$pi.c - x$C * x$pi.d)/(x$C + x$D)^2
# # Asymptotic standard error: sqrt(sigma2)
# gamma_sigma2 <- sum(tab * psi^2) - sum(tab * psi)^2
#
# pr2 <- 1 - (1 - conf.level)/2
# ci <- qnorm(pr2) * sqrt(gamma_sigma2) * c(-1, 1) + gamma
# result <- c(gamma = gamma, lwr.ci=max(ci[1], -1), ups.ci=min(ci[2], 1))
result <- NA
}
return(result)
}
TablePearson <- function(x, scores.type="table") {
# based on Lecoutre
# https://stat.ethz.ch/pipermail/r-help/2005-July/076371.html
# but the test might not be correctly implemented (negative values in sqrt)
# Statistic
sR <- scores(x, 1, scores.type)
sC <- scores(x, 2, scores.type)
n <- sum(x)
Rbar <- sum(apply(x, 1, sum) * sR) / n
Cbar <- sum(apply(x, 2, sum) * sC) / n
ssr <- sum(x * (sR-Rbar)^2)
ssc <- sum(t(x) * (sC-Cbar)^2)
tmpij <- outer(sR, sC, FUN=function(a,b) return((a-Rbar)*(b-Cbar)))
ssrc <- sum(x*tmpij)
v <- ssrc
w <- sqrt(ssr*ssc)
r <- v/w
return(r)
}
TableSpearman <- function(x, scores.type="table"){
# following:
# https://stat.ethz.ch/pipermail/r-help/2005-July/076371.html
# tablespearman=function(x)
# {
# # Details algorithme manuel SAS PROC FREQ page 540
# # Statistic
# n=sum(x)
# nr=nrow(x)
# nc=ncol(x)
# tmpd=cbind(expand.grid(1:nr,1:nc))
# ind=rep(1:(nr*nc),as.vector(x))
# tmp=tmpd[ind,]
# rhos=cor(apply(tmp,2,rank))[1,2]
# # ASE
# Ri=scores(x,1,"ranks")- n/2
# Ci=scores(x,2,"ranks")- n/2
# sr=apply(x,1,sum)
# sc=apply(x,2,sum)
# F=n^3 - sum(sr^3)
# G=n^3 - sum(sc^3)
# w=(1/12)*sqrt(F*G)
# vij=data
# for (i in 1:nrow(x))
# {
# qi=0
# if (i<nrow(x))
# {
# for (k in i:nrow(x)) qi=qi+sum(x[k,]*Ci)
# }
# }
# for (j in 1:ncol(x))
# {
# qj=0
# if (j<ncol(x))
# {
# for (k in j:ncol(x)) qj=qj+sum(x[,k]*Ri)
# }
# vij[i,j]=n*(Ri[i]*Ci[j] +
# 0.5*sum(x[i,]*Ci)+0.5*sum(data[,j]*Ri) +qi+qj)
# }
#
#
# v=sum(data*outer(Ri,Ci))
# wij=-n/(96*w)*outer(sr,sc,FUN=function(a,b) return(a^2*G+b^2*F))
# zij=w*vij-v*wij
# zbar=sum(data*zij)/n
# vard=(1/(n^2*w^4))*sum(x*(zij-zbar)^2)
# ASE=sqrt(vard)
# # Test
# vbar=sum(x*vij)/n
# p1=sum(x*(vij-vbar)^2)
# p2=n^2*w^2
# var0=p1/p2
# stat=rhos/sqrt(var0)
#
# # Output
# out=list(estimate=rhos,ASE=ASE,name="Spearman
# correlation",bornes=c(-1,1))
# class(out)="ordtest"
# return(out)
# }
#
# #tablespearman(data)
}
# all association measures combined
Assocs <- function(x, conf.level = 0.95, verbose=NULL){
if(is.null(verbose)) verbose <- "3"
if(verbose != "3") conf.level <- NA
res <- rbind(
# "Phi Coeff." = c(Phi(x), NA, NA)
"Contingency Coeff." = c(ContCoef(x),NA, NA)
)
if(is.na(conf.level)){
res <- rbind(res, "Cramer V" = c(CramerV(x), NA, NA))
res <- rbind(res, "Kendall Tau-b" = c(KendallTauB(x), NA, NA))
} else {
res <- rbind(res, "Cramer V" = CramerV(x, conf.level=conf.level))
res <- rbind(res, "Kendall Tau-b" = c(KendallTauB(x, conf.level=conf.level)))
}
if(verbose=="3") {
# # this is from boot::corr combined with ci logic from cor.test
# r <- boot::corr(d=CombPairs(1:nrow(x), 1:ncol(x)), as.vector(x))
# the boot::corr does not respect ordinal values in dimnames
# so do it ourselves
r <- TablePearson(x)
r.ci <- CorCI(rho = r, n = sum(x), conf.level = conf.level)
res <- rbind(res
, "Goodman Kruskal Gamma" = GoodmanKruskalGamma(x, conf.level=conf.level)
# , "Kendall Tau-b" = KendallTauB(x, conf.level=conf.level)
, "Stuart Tau-c" = StuartTauC(x, conf.level=conf.level)
, "Somers D C|R" = SomersDelta(x, direction="column", conf.level=conf.level)
, "Somers D R|C" = SomersDelta(x, direction="r", conf.level=conf.level)
# , "Pearson Correlation" =c(cor.p$estimate, lwr.ci=cor.p$conf.int[1], upr.ci=cor.p$conf.int[2])
, "Pearson Correlation" =c(r.ci[1], lwr.ci=r.ci[2], upr.ci=r.ci[3])
, "Spearman Correlation" = SpearmanRho(x, conf.level=conf.level)
, "Lambda C|R" = Lambda(x, direction="column", conf.level=conf.level)
, "Lambda R|C" = Lambda(x, direction="row", conf.level=conf.level)
, "Lambda sym" = Lambda(x, direction="sym", conf.level=conf.level)
, "Uncertainty Coeff. C|R" = UncertCoef(x, direction="column", conf.level=conf.level)
, "Uncertainty Coeff. R|C" = UncertCoef(x, direction="row", conf.level=conf.level)
, "Uncertainty Coeff. sym" = UncertCoef(x, direction="sym", conf.level=conf.level)
, "Mutual Information" = c(MutInf(x),NA,NA)
) }
if(verbose=="3")
dimnames(res)[[2]][1] <- "estimate"
else
dimnames(res)[[2]] <- c("estimate", "lwr.ci", "upr.ci")
class(res) <- c("Assocs", class(res))
return(res)
}
print.Assocs <- function(x, digits=4, ...){
out <- apply(round(x, digits), 2, Format, digits=digits)
if(nrow(x) == 3){
} else {
out[c(1,16), 2:3] <- " -"
}
dimnames(out) <- dimnames(x)
print(data.frame(out), quote=FALSE)
}
## This is an exact copy from Hmisc
## Changes since sent to statlib: improved printing N matrix in print.hoeffd
HoeffD <- function(x, y) {
phoeffd <- function(d, n) {
d <- as.matrix(d); n <- as.matrix(n)
b <- d + 1/36/n
z <- .5*(pi^4)*n*b
zz <- as.vector(z)
zz[is.na(zz)] <- 1e30 # so approx won't bark
tabvals <- c(5297,4918,4565,4236,3930,
3648,3387,3146,2924,2719,2530,2355,
2194,2045,1908,1781,1663,1554,1453,
1359,1273,1192,1117,1047,0982,0921,
0864,0812,0762,0716,0673,0633,0595,
0560,0527,0496,0467,0440,0414,0390,
0368,0347,0327,0308,0291,0274,0259,
0244,0230,0217,0205,0194,0183,0173,
0163,0154,0145,0137,0130,0123,0116,
0110,0104,0098,0093,0087,0083,0078,
0074,0070,0066,0063,0059,0056,0053,
0050,0047,0045,0042,0025,0014,0008,
0005,0003,0002,0001)/10000
P <- ifelse(z<1.1 | z>8.5, pmax(1e-8,pmin(1,exp(.3885037-1.164879*z))),
matrix(approx(c(seq(1.1, 5,by=.05),
seq(5.5,8.5,by=.5)),
tabvals, zz)$y,
ncol=ncol(d)))
dimnames(P) <- dimnames(d)
P
}
if(!missing(y))
x <- cbind(x, y)
x[is.na(x)] <- 1e30
storage.mode(x) <-
# if(.R.)
"double"
# else
# "single"
p <- as.integer(ncol(x))
if(p<1)
stop("must have >1 column")
n <- as.integer(nrow(x))
if(n<5)
stop("must have >4 observations")
h <-
# if(.R.)
.Fortran("hoeffd", x, n, p, hmatrix=double(p*p), aad=double(p*p),
maxad=double(p*p), npair=integer(p*p),
double(n), double(n), double(n), double(n), double(n),
PACKAGE="DescTools")
# else
# .Fortran("hoeffd", x, n, p, hmatrix=single(p*p), npair=integer(p*p),
# single(n), single(n), single(n), single(n), single(n),
# single(n), integer(n))
nam <- dimnames(x)[[2]]
npair <- matrix(h$npair, ncol=p)
aad <- maxad <- NULL
# if(.R.) {
aad <- matrix(h$aad, ncol=p)
maxad <- matrix(h$maxad, ncol=p)
dimnames(aad) <- dimnames(maxad) <- list(nam, nam)
# }
h <- matrix(h$hmatrix, ncol=p)
h[h>1e29] <- NA
dimnames(h) <- list(nam, nam)
dimnames(npair) <- list(nam, nam)
P <- phoeffd(h, npair)
diag(P) <- NA
structure(list(D=30*h, n=npair, P=P, aad=aad, maxad=maxad), class="HoeffD")
}
print.HoeffD <- function(x, ...)
{
cat("D\n")
print(round(x$D,2))
if(length(aad <- x$aad)) {
cat('\navg|F(x,y)-G(x)H(y)|\n')
print(round(aad,4))
}
if(length(mad <- x$maxad)) {
cat('\nmax|F(x,y)-G(x)H(y)|\n')
print(round(mad,4))
}
n <- x$n
if(all(n==n[1,1]))
cat("\nn=",n[1,1],"\n")
else {
cat("\nn\n")
print(x$n)
}
cat("\nP\n")
P <- x$P
P <- ifelse(P<.0001,0,P)
p <- format(round(P,4))
p[is.na(P)] <- ""
print(p, quote=FALSE)
invisible()
}
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