Nothing
R/descrip_utils.R
In rigr: Regression, Inference, and General Data Analysis Tools in R
Defines functions
describe_stratified_list
describe_stratified_matrix
describe_stratified_surv
describe_stratified_date
describe_stratified_vector
describe_surv
describe_vector
mean_KM
quantile_KM
sum_KM
KM
is.Date
# ------------------------------------------------------------------------------
# General helper functions
# ------------------------------------------------------------------------------
#' Check if an object is a "Date" object
#'
#' @param x the variable
#'
#' @return A logical flag indicating whether the variable is a "Date" variable.
#'
#' @keywords internal
#' @noRd
is.Date <- function(x) {
inherits(x, "Date")
}
# ------------------------------------------------------------------------------
# Survival-object-specific helper functions
# ------------------------------------------------------------------------------
#' Compute Kaplan-Meier estimates of the survival curve
#'
#' @param x the data
#'
#' @return Kaplan-Meier estimates of the survival curve
#'
#' @keywords internal
#' @noRd
KM <- function(x) {
if (!survival::is.Surv(x))
stop("x must be a Surv object")
x <- x[!is.na(x)]
# time of observations
obs <- x[, 1]
# event status (yes/no, 1/0)
ev <- x[, 2]
# censored observation (yes/no, 1/0)
ce <- 1 - ev
if (length(obs) == 0)
stop("No data to estimate survival curve")
N <- length(obs)
# if there are no observations at time 0, add in values for time zero (no events)
if (!any(obs == 0)) {
obs <- c(0, obs)
ev <- c(0, ev)
ce <- c(0, ce)
}
# reorder vectors according to observation time
i <- order(obs, 1 - ev)
obs <- obs[i]
ev <- ev[i]
ce <- ce[i]
ev <- rev(cumsum(rev(ev)))
ce <- rev(cumsum(rev(ce)))
# cumulative number of people at risk for event
n <- ce + ev
# remove duplicate observations
i <- !duplicated(obs)
obs <- obs[i]
n <- n[i]
ev <- ev[i]
ev <- ev - c(ev[-1], 0)
ce <- ce[i]
ce <- ce - c(ce[-1], 0)
v <- N * cumsum(ev/(n - ev)/n)
S <- exp(cumsum(log(1 - ev/n)))
if (is.na(S[length(S)]))
S[length(S)] <- 0
rslt <- data.frame(t = obs, atrisk = n, events = ev,
censored = ce, S = S, v = v)
class(rslt) <- c("KM", "data.frame")
rslt
}
#' Compute a sum over a Kaplan-Meier curve
#'
#' @param x an object of class \code{KM} (from a call to \code{KM})
#' @param times a vector of times to use in computation
#' @param rightCtsCDF logical, is the cdf right-continuous? (defaults to \code{TRUE})
#'
#' @return the summed survival curve, over the entered times
#'
#' @keywords internal
#' @noRd
sum_KM <- function(x, times, rightCtsCDF = TRUE) {
if (!inherits(x, "KM"))
stop("x must be a KM object")
if (rightCtsCDF) {
rslt <- as.vector(apply(matrix(rep(times, each = length(x$t)),
length(x$t)) >= x$t, 2, sum)) + 1
}
else rslt <- as.vector(apply(matrix(rep(times, each = length(x$t)),
length(x$t)) > x$t, 2, sum)) + 1
if (x$S[length(x$S)] > 0)
rslt[times > x$t[length(x$t)]] <- NA
c(1, x$S)[rslt]
}
#' Compute quantiles of the Kaplan-Meier curve
#'
#' @inheritParams sum_KM
#' @param probs a vector of proportions of interest for the quantiles
#'
#' @return quantiles of the estimated Kaplan-Meier curve
#'
#' @keywords internal
#' @noRd
quantile_KM <- function(x, probs) {
rslt <- length(probs)
for (i in 1:length(probs)) {
p <- 1 - probs[i]
j <- abs(x$S - p) < 1e-15 & x$events > 0
if (any(j)) {
if (abs(p - min(x$S)) < 1e-15) {
rslt[i] <- (x$t[j] + max(x$t))/2
}
else {
rslt[i] <- (x$t[j] + min(x$t[x$t > x$t[j] & x$events > 0]))/2
}
}
else {
j <- sum(x$S > p)
if (j == length(x$S) | p == 1) {
rslt[i] <- NA
}
else rslt[i] <- x$t[j + 1]
}
}
rslt
}
#' Compute the restricted mean survival time
#'
#' @inheritParams sum_KM
#' @param restriction a value used for computing restricted means, standard deviations,
#' and geometric means with censored time-to-event data. The default value of
#' \code{Inf} will cause restrictions at the highest observation. Note that the
#' same value is used for all variables of class \code{Surv}.
#'
#' @return the restricted mean survival time
#' @keywords internal
#' @noRd
mean_KM <- function(x, restriction = Inf) {
if (length(restriction) == 1)
restriction <- c(x$t[1] - 1, restriction)
if (restriction[2] == Inf)
restriction[2] <- x$t[length(x$t)]
tms <- c(restriction[1], x$t[x$t > restriction[1] & x$t < restriction[2]],
restriction[2])
s <- sum_KM(x, restriction)
s <- c(s[1], x$S[x$t > restriction[1] & x$t < restriction[2]], s[2])
ne <- tms <= 0
po <- tms >= 0
neS <- 1 - s[ne]
neX <- abs(c(diff(tms[ne]), 0))
neI <- neS != 0 & neX != 0
if (sum(neI) > 0)
rslt <- -sum(neS[neI] * neX[neI])
else rslt <- 0
poS <- s[po]
poX <- c(diff(tms[po]), 0)
poI <- poS != 0 & poX != 0
if (sum(poI) > 0)
rslt <- rslt + sum(poS[poI] * poX[poI])
attr(rslt, "restriction") <- restriction
rslt
}
# ------------------------------------------------------------------------------
# Compute descriptive statistics for different object classes
# ------------------------------------------------------------------------------
#' Descriptive Statistics for a Vector of Data
#' @param x a vector of data
#' @param probs a vector of proportions to use in describing (e.g., 0.25, 0.5)
#' @param thresholds a vector of thresholds used to dichotomize variables.
#' @param geometricMean logical, should we compute the geomtric mean? Defaults
#' to \code{FALSE}.
#' @param geomInclude if not \code{FALSE}, includes the geometric mean in the
#' descriptive statistics. Default is \code{FALSE}.
#' @param replaceZeroes if not \code{FALSE}, this indicates a value to be used
#' in place of zeroes when computing a geometric mean. If \code{TRUE}, a value
#' equal to one-half the lowest nonzero value is used. If a numeric value is
#' supplied, that value is used for all variables.
#'
#' @return A matrix of descriptive statistics
#'
#' @keywords internal
#' @noRd
describe_vector <- function(x, probs = c(0.25, 0.5, 0.75), thresholds = NULL,
geometricMean = FALSE, geomInclude = FALSE,
replaceZeroes = FALSE) {
if (is.character(x)) {
x <- as.factor(x)
}
if (is.factor(x) | is.logical(x)) {
x <- as.numeric(x)
}
if (!geomInclude) {
geometricMean <- geomInclude
}
ntholds <- ifelse(is.null(thresholds), 0, dim(thresholds)[1])
probs <- sort(unique(c(probs, 0, 1)))
# add sample size
rslt <- length(x)
if (rslt == 0) {
rslt <- c(rslt, rep(NaN, 7 + length(probs) + ntholds))
}
else {
# add number missing
u <- is.na(x)
rslt <- c(rslt, sum(u))
# only look at non-missing observations for remaining summaries
x <- x[!u]
if (length(x) == 0 | is.character(x)) {
if (!geomInclude) {
rslt <- c(rslt, rep(NA, 6 + length(probs) + ntholds))
} else {
rslt <- c(rslt, rep(NA, 7 + length(probs) + ntholds))
}
} else {
if (geomInclude) {
# add mean, sd, geometric mean, quantiles
rslt <- c(rslt, mean(x), stats::sd(x), ifelse1(
geometricMean,
exp(mean(log(ifelse(x == 0, replaceZeroes, x)))),
NA
), stats::quantile(x, probs))
} else {
# add mean, sd, quantiles
rslt <- c(rslt, mean(x), stats::sd(x), stats::quantile(x, probs))
}
if (ntholds > 0) {
# add thresholds if any are specified
for (j in 1:ntholds) {
u <- ifelse1(
thresholds[j, 1] == 0, x > thresholds[j, 2], x >= thresholds[j, 2]
) & ifelse1(
thresholds[j, 3] == 0, x < thresholds[j, 4], x <= thresholds[j, 4]
)
rslt <- c(rslt, mean(u))
}
}
# add in firstEvent and lastEvent
rslt <- c(rslt, Inf, NA, NA)
}
}
if (length(x) > 0) {
# add in a 0 if the vector is not a date
rslt <- matrix(c(rslt, 0), 1)
}
else {
rslt <- matrix(rslt, 1)
}
# set quantile names
qnames <- paste(format(100 * probs), "%", sep = "")
qnames[probs == 0.5] <- " Mdn"
qnames[probs == 0] <- " Min"
qnames[probs == 1] <- " Max"
tnames <- NULL
# set threshold column names
if (ntholds > 0) {
tholds <- thresholds
tholds[tholds == Inf | tholds == -Inf] <- 0
tnames <- paste(
sep = "", "Pr", ifelse(
thresholds[, 2] == -Inf,
paste(sep = "", ifelse(thresholds[, 3] == 0, "<", "<="), format(tholds[, 4])),
ifelse(
thresholds[, 4] == Inf,
paste(sep = "", ifelse(thresholds[, 1] == 0, ">", ">="), format(tholds[, 2])),
paste(
sep = "", ifelse(thresholds[, 1] == 0, "(", "["),
format(tholds[, 2]), ",", format(tholds[, 4]),
ifelse(thresholds[, 3] == 0, ")", "]")
)
)
)
)
}
if (geomInclude) {
dimnames(rslt) <- list("", c("N", "Msng", "Mean",
"Std Dev", "Geom Mn", qnames, tnames, "restriction",
"firstEvent", "lastEvent", "isDate"))
}
else {
dimnames(rslt) <- list("", c("N", "Msng", "Mean",
"Std Dev", qnames, tnames, "restriction", "firstEvent",
"lastEvent", "isDate"))
}
rslt
}
#' Describe a Survival variable
#'
#' @param x the variable of interest
#' @param resctriction a value used for computing restricted means,
#' standard deviations, and geometric means with censored time to event data.
#' The default value of \code{Inf} will cause restrictions at the highest
#' observation. Note that the same value is used for all variables of class \code{Surv}.
#' @inheritParams describe_vector
#'
#' @return A matrix with descriptive statistics for the Surv object
#' @keywords internal
#' @noRd
describe_surv <- function(x, probs = c(0.25, 0.5, 0.75), thresholds = NULL,
geometricMean = FALSE, geomInclude = FALSE,
replaceZeroes = FALSE, restriction = Inf) {
if (!survival::is.Surv(x))
stop("x must be a Surv object")
ntholds <- if (is.null(thresholds))
0
else dim(thresholds)[1]
probs <- sort(unique(c(probs, 0, 1)))
rslt <- dim(x)[1]
if (rslt == 0) {
rslt <- c(rslt, rep(NaN, 7 + length(probs) + ntholds))
}
else {
u <- is.na(x)
rslt <- c(rslt, sum(u))
x <- x[!u]
if (dim(x)[1] == 0) {
rslt <- c(rslt, rep(NA, 6 + length(probs) + ntholds))
}
else {
z <- KM(x)
tmp1 <- mean_KM(z, restriction)
x2 <- x
x2[, 1] <- x2[, 1]^2
z2 <- KM(x2)
# compute standard deviation as \sqrt{E[X^2] - E[X]^2}
tmp2 <- sqrt(mean_KM(z2, restriction^2) - tmp1^2)
if (geometricMean) {
x2 <- x
x2[, 1] <- ifelse(x2[, 1] == 0, log(replaceZeroes),
log(x2[, 1]))
z2 <- KM(x2)
tmp3 <- exp(mean_KM(z2, log(restriction)))
}
else tmp3 <- NA
if (any(x[, 2] == 1)) {
firstEvent <- min(x[x[, 2] == 1, 1])
lastEvent <- max(x[x[, 2] == 1, 1])
}
else {
firstEvent <- Inf
lastEvent <- -Inf
}
if (geomInclude) {
rslt <- c(rslt, tmp1, tmp2, tmp3, min(x[, 1]),
quantile_KM(z, probs[-c(1, length(probs))]), max(x[,
1]))
}
else {
rslt <- c(rslt, tmp1, tmp2, min(x[, 1]), quantile_KM(z,
probs[-c(1, length(probs))]), max(x[, 1]))
}
if (ntholds > 0) {
for (j in 1:ntholds) {
rslt <- c(rslt, ifelse1(thresholds[j, 1] ==
0, sum_KM(z, thresholds[j, 2]), sum_KM(z, thresholds[j,
2], FALSE)) - ifelse1(thresholds[j, 4] == Inf,
0, ifelse1(thresholds[j, 3] == 0, sum_KM(z,
thresholds[j, 4], FALSE), sum_KM(z, thresholds[j,
4]))))
}
}
rslt <- c(rslt, attr(tmp1, "restriction")[2],
firstEvent, lastEvent)
}
}
rslt <- matrix(c(rslt, 0), 1)
qnames <- paste(format(100 * probs), "%", sep = "")
qnames[probs == 0.5] <- " Mdn"
qnames[probs == 0] <- " Min"
qnames[probs == 1] <- " Max"
tnames <- NULL
if (ntholds > 0) {
tholds <- thresholds
tholds[tholds == Inf | tholds == -Inf] <- 0
tnames <- paste(sep = "", "Pr", ifelse(thresholds[,
2] == -Inf, paste(sep = "", ifelse(thresholds[,
3] == 0, "<", "<="), format(tholds[, 4])), ifelse(thresholds[,
4] == Inf, paste(sep = "", ifelse(thresholds[,
1] == 0, ">", ">="), format(tholds[, 2])), paste(sep = "",
ifelse(thresholds[, 1] == 0, "(", "["), format(tholds[,
2]), ",", format(tholds[, 4]), ifelse(thresholds[,
3] == 0, ")", "]")))))
}
if (geomInclude) {
dimnames(rslt) <- list("", c("N", "Msng", "Mean",
"Std Dev", "Geom Mn", qnames, tnames, "restriction",
"firstEvent", "lastEvent", "isDate"))
}
else {
dimnames(rslt) <- list("", c("N", "Msng", "Mean",
"Std Dev", qnames, tnames, "restriction", "firstEvent",
"lastEvent", "isDate"))
}
rslt
}
#' Describe a vector variable within strata
#'
#' @param x the vector of data to describe
#' @param strata the strata
#' @param subset a vector indicating a subset within which descriptive statistics
#' are desired.
#' @param probs a vector of proportions to use for quantiles
#' @param thresholds a vector of thresholds used to dichotomize variables.
#' @param geomInclude if not \code{FALSE} (the default), includes the geometric mean
#' in the descriptive statistics.
#' @param replaceZeroes if not \code{FALSE} (the default), this indicates a value
#' to be used in place of zeroes when computing a geometric mean. If \code{TRUE},
#' a value equal to one-half the lowest nonzero value is used. If a numeric value
#' is supplied, that value is used for all variables.
#'
#' @return the descriptive statistics within strata
#'
#' @keywords internal
#' @noRd
describe_stratified_vector <- function(x, strata, subset,
probs = c(0.25, 0.5, 0.75),
thresholds = NULL, geomInclude = FALSE,
replaceZeroes = FALSE) {
if (is.null(subset))
subset <- rep(TRUE, length(x))
if (length(x) != length(subset))
stop("length of variables must match length of subset")
if (is.null(strata))
strata <- rep(1, length(x))
if (length(x) != length(strata))
stop("length of variables must match length of strata")
x <- x[subset]
if (is.factor(x) | all(x[!is.na(x)] %in% c(0, 1))) {
geometricMean <- FALSE
} else {
geometricMean <- !any(x[!is.na(x)] < 0)
}
if (is.logical(replaceZeroes)) {
if (!replaceZeroes | is.factor(x) | all(x[!is.na(x)] %in%
c(0, 1))) {
replaceZeroes <- NA
} else {
replaceZeroes <- min(x[!is.na(x) & x > 0])/2
}
}
strata <- strata[subset]
s <- sort(unique(strata))
rslt <- describe_vector(x, probs, thresholds, geometricMean,
geomInclude, replaceZeroes)
if (length(s) > 1) {
for (i in s) rslt <- rbind(rslt, describe_vector(x[strata ==
i & !is.na(strata)], probs, thresholds, geometricMean,
geomInclude, replaceZeroes))
if (any(is.na(strata))) {
rslt <- rbind(rslt, describe_vector(x[is.na(strata)],
probs, thresholds, geometricMean, geomInclude,
replaceZeroes))
dimnames(rslt)[[1]] <- format(c("All", paste(" Str",
format(c(format(s), "NA")))))
}
else dimnames(rslt)[[1]] <- format(c("All", paste(" Str",
format(s))))
}
rslt
}
#' Describe a Date object within strata
#'
#' @inheritParams describe_stratified_vector
#'
#' @return descriptive statistics within strata for a Date object
#'
#' @keywords internal
#' @noRd
describe_stratified_date <- function(x, strata, subset,
probs = c(0.25, 0.5, 0.75),
thresholds = NULL, geomInclude = FALSE,
replaceZeroes = FALSE) {
if (!is.Date(x))
stop("x must be a Date object")
xi <- as.integer(x)
rslt <- describe_stratified_vector(xi, strata, subset, probs, thresholds,
geomInclude, replaceZeroes)
rslt[, "isDate"] <- 1
rslt
}
#' Describe a stratified Surv object
#'
#' @inheritParams describe_stratified_vector
#' @inheritParams describe_surv
#'
#' @return descriptive statistics within strata for a Surv object
#'
#' @keywords internal
#' @noRd
describe_stratified_surv <- function(x, strata, subset,
probs = c(0.25, 0.5, 0.75),
thresholds = NULL, geomInclude = FALSE,
replaceZeroes = FALSE, restriction = Inf) {
if (!survival::is.Surv(x))
stop("x must be a Surv object")
n <- dim(x)[1]
if (is.null(subset))
subset <- rep(TRUE, n)
if (n != length(subset))
stop("length of variables must match length of subset")
if (is.null(strata))
strata <- rep(1, n)
if (n != length(strata))
stop("length of variables must match length of strata")
x <- x[subset]
if (geomInclude) {
geometricMean <- !any(x[!is.na(x), 1] < 0)
}
else {
geometricMean <- FALSE
}
if (is.logical(replaceZeroes)) {
if (replaceZeroes) {
replaceZeroes <- min(x[!is.na(x) & x[, 1] > 0, 1])/2
} else {
replaceZeroes <- NA
}
}
strata <- strata[subset]
s <- sort(unique(strata))
rslt <- describe_surv(x, probs, thresholds, geomInclude, geometricMean,
replaceZeroes, restriction)
if (length(s) > 1) {
for (i in s) {
rslt <- rbind(rslt, describe_surv(x[strata == i & !is.na(strata)],
probs, thresholds, geomInclude,
geometricMean, replaceZeroes, restriction))
}
if (any(is.na(strata))) {
rslt <- rbind(rslt, describe_surv(x[is.na(strata)], probs,
thresholds, geomInclude, geometricMean,
replaceZeroes, restriction))
dimnames(rslt)[[1]] <- format(c("All",
paste(" Str", format(c(format(s), "NA")))))
} else {
dimnames(rslt)[[1]] <- format(c("All", paste(" Str", format(s))))
}
}
rslt
}
#' Describe a matrix object within strata
#'
#' @inheritParams describe_stratified_vector
#'
#' @return descriptive statistics within strata for a matrix object
#'
#' @keywords internal
#' @noRd
describe_stratified_matrix <- function(x, strata, subset,
probs = c(0.25, 0.5, 0.75),
thresholds = NULL, geomInclude = FALSE,
replaceZeroes = FALSE) {
if (!is.matrix(x)) {
stop("x must be a matrix")
}
p <- dim(x)[2]
nms <- dimnames(x)[[2]]
if (is.null(nms)) {
nms <- paste("V", 1:p, sep = "")
}
rslt <- NULL
for (i in 1:p) {
rslt <- rbind(
rslt,
describe_stratified_vector(x[, i], strata, subset, probs,
thresholds, geomInclude, replaceZeroes)
)
}
dimnames(rslt)[[1]] <- paste(format(rep(nms, each = (dim(rslt)[1])/p)), dimnames(rslt)[[1]])
rslt
}
#' Describe a list object within strata
#'
#' @inheritParams describe_stratified_surv
#'
#' @return descriptive statistics for each element of the list within strata
#'
#' @keywords internal
#' @noRd
describe_stratified_list <- function(x, strata, subset,
probs = c(0.25, 0.5, 0.75),
thresholds = NULL, geomInclude = FALSE,
replaceZeroes = FALSE, restriction = Inf) {
if (!is.list(x)) {
stop("x must be a list")
}
p <- length(x)
nms <- names(x)
if (is.null(nms)) {
nms <- paste("V", 1:p, sep = "")
}
rslt <- NULL
for (i in 1:p) {
if (survival::is.Surv(x[[i]])) {
rslt <- rbind(rslt, describe_stratified_surv(x[[i]], strata,
subset, probs, thresholds,
geomInclude, replaceZeroes,
restriction))
} else if (is.Date(x[[i]])) {
rslt <- rbind(rslt, describe_stratified_date(x[[i]], strata, subset,
probs, thresholds,
geomInclude, replaceZeroes))
} else {
rslt <- rbind(rslt, describe_stratified_vector(x[[i]], strata, subset,
probs, thresholds,
geomInclude, replaceZeroes))
}
}
dimnames(rslt)[[1]] <- paste(format(rep(nms, each = (dim(rslt)[1])/p)), dimnames(rslt)[[1]])
rslt
}
#' Print method for class uDescriptives
#' @noRd
#' @export
print.uDescriptives <- function (x, ..., sigfigs=max(3,getOption("digits")-3),width=9,nonsci.limit=5, print.it= TRUE) {
#
# prints CI or exp(CI) to specified sigfigs
# rounds exp(Est) to same number of decimal figures as exp(CI)
# prints Std Err or Robust SE to specified sigfigs
# rounds Estimate to same number of decimal figures as StdErr or Robust SE
# prints F stat to specified sigfigs
# prints P value to number of decimal figures as nonsci.limit unless < 10^-nonsci.limit when "< .00001" used
# centers all but df and P value, which is right justified
cmptRoundDigits <- function (x, sf) {
y <- max(abs(x),na.rm=TRUE)
if (y==0) {
sf
} else {
y <- trunc(log(y) / log(10)) - (y < 1)
max(0,sf - y - (y < sf))
}
}
frmtCol <- function (x, sf, nonsci.limit, colwidth=9, append="") {
rslt <- NULL
for (i in 1:length(x)) {
if (is.na(x[i])) {
tmp <- "NA"
} else {
rd <- cmptRoundDigits (x[i], sf)
if (rd <= nonsci.limit & abs(x[i]) < 10^nonsci.limit) {
tmp <- format(round(x[i],rd),nsmall=rd,width=1)
} else {
tmp <- format(round(x[i],rd), digits=sf, scientific=TRUE, width=1)
}
}
rslt <- c(rslt,ifelse(x[i]<0,tmp,paste(" ",tmp,sep="")))
}
rslt <- paste(rslt,append,sep="")
format(rslt, justify="centre", width=colwidth)
}
ncol <- dim(x)[2]
meancol <- (1:ncol)[dimnames(x)[[2]]=="Mean"]
mincol <- (1:ncol)[dimnames(x)[[2]]==" Min"]
maxcol <- (1:ncol)[dimnames(x)[[2]]==" Max"]
censMin <- x[,"firstEvent"] > x[,mincol]
censMin[is.na(censMin)] <- FALSE
censMax <- x[,"lastEvent"] < x[,maxcol]
censMax[is.na(censMax)] <- FALSE
if (!any(censMax)) {
restriction <- NULL
} else {
restriction <- frmtCol(x[,"restriction"],sigfigs,nonsci.limit,1,")")
restriction <- paste("(R",restriction,sep="")
restriction[!censMax] <- ""
restriction <- format(restriction, justify="left")
}
frmtCoefficients <- format(x[,1:(ncol-4),drop=FALSE])
for (j in 1:2) frmtCoefficients[,j] <- format (x[,j],justify="right",width=5)
frmtCoefficients[,mincol] <- frmtCol (x[,mincol],sigfigs,nonsci.limit,width,ifelse(censMin,"+",""))
for (j in 3:5) frmtCoefficients[,j] <- frmtCol (x[,j],sigfigs,nonsci.limit,width,ifelse(censMax,"+",""))
if(any(dimnames(x)[[2]] == "Geom Mn")){
indx <- 7:(ncol-4)
} else {
indx <- 6:(ncol-4)
}
indx <- indx[indx != maxcol]
for (j in indx) frmtCoefficients[,j] <- frmtCol (x[,j],sigfigs,nonsci.limit,width)
frmtCoefficients[,maxcol] <- frmtCol (x[,maxcol],sigfigs,nonsci.limit,width,ifelse(censMax,"+",""))
## set a temp variable to check if any are NA
dateBool <- any(is.na(x[,"isDate"]))
if(dateBool){
} else {
if (any(x[,"isDate"]==1)) {
xformCol <- c(3,5:(dim(x)[2]-4))
for (j in 1:length(xformCol)) {
if (substring(dimnames(x)[[2]][xformCol[j]],1,2)=="Pr") xformCol[j] <- NA
}
xformCol <- xformCol[!is.na(xformCol)]
orgn <- "1970-01-01"
frmtCoefficients[x[,"isDate"]==1,xformCol] <- format(as.Date(x[x[,"isDate"]==1,xformCol],orgn))
}
}
if (!is.null(restriction)) frmtCoefficients <- cbind(frmtCoefficients[,1:2,drop=FALSE],
"Restrict"=restriction,frmtCoefficients[,-(1:2),drop=FALSE])
if(print.it) print(frmtCoefficients,quote=FALSE)
invisible(frmtCoefficients)
}
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Defines functions describe_stratified_list describe_stratified_matrix describe_stratified_surv describe_stratified_date describe_stratified_vector describe_surv describe_vector mean_KM quantile_KM sum_KM KM is.Date
# ------------------------------------------------------------------------------
# General helper functions
# ------------------------------------------------------------------------------
#' Check if an object is a "Date" object
#'
#' @param x the variable
#'
#' @return A logical flag indicating whether the variable is a "Date" variable.
#'
#' @keywords internal
#' @noRd
is.Date <- function(x) {
inherits(x, "Date")
}
# ------------------------------------------------------------------------------
# Survival-object-specific helper functions
# ------------------------------------------------------------------------------
#' Compute Kaplan-Meier estimates of the survival curve
#'
#' @param x the data
#'
#' @return Kaplan-Meier estimates of the survival curve
#'
#' @keywords internal
#' @noRd
KM <- function(x) {
if (!survival::is.Surv(x))
stop("x must be a Surv object")
x <- x[!is.na(x)]
# time of observations
obs <- x[, 1]
# event status (yes/no, 1/0)
ev <- x[, 2]
# censored observation (yes/no, 1/0)
ce <- 1 - ev
if (length(obs) == 0)
stop("No data to estimate survival curve")
N <- length(obs)
# if there are no observations at time 0, add in values for time zero (no events)
if (!any(obs == 0)) {
obs <- c(0, obs)
ev <- c(0, ev)
ce <- c(0, ce)
}
# reorder vectors according to observation time
i <- order(obs, 1 - ev)
obs <- obs[i]
ev <- ev[i]
ce <- ce[i]
ev <- rev(cumsum(rev(ev)))
ce <- rev(cumsum(rev(ce)))
# cumulative number of people at risk for event
n <- ce + ev
# remove duplicate observations
i <- !duplicated(obs)
obs <- obs[i]
n <- n[i]
ev <- ev[i]
ev <- ev - c(ev[-1], 0)
ce <- ce[i]
ce <- ce - c(ce[-1], 0)
v <- N * cumsum(ev/(n - ev)/n)
S <- exp(cumsum(log(1 - ev/n)))
if (is.na(S[length(S)]))
S[length(S)] <- 0
rslt <- data.frame(t = obs, atrisk = n, events = ev,
censored = ce, S = S, v = v)
class(rslt) <- c("KM", "data.frame")
rslt
}
#' Compute a sum over a Kaplan-Meier curve
#'
#' @param x an object of class \code{KM} (from a call to \code{KM})
#' @param times a vector of times to use in computation
#' @param rightCtsCDF logical, is the cdf right-continuous? (defaults to \code{TRUE})
#'
#' @return the summed survival curve, over the entered times
#'
#' @keywords internal
#' @noRd
sum_KM <- function(x, times, rightCtsCDF = TRUE) {
if (!inherits(x, "KM"))
stop("x must be a KM object")
if (rightCtsCDF) {
rslt <- as.vector(apply(matrix(rep(times, each = length(x$t)),
length(x$t)) >= x$t, 2, sum)) + 1
}
else rslt <- as.vector(apply(matrix(rep(times, each = length(x$t)),
length(x$t)) > x$t, 2, sum)) + 1
if (x$S[length(x$S)] > 0)
rslt[times > x$t[length(x$t)]] <- NA
c(1, x$S)[rslt]
}
#' Compute quantiles of the Kaplan-Meier curve
#'
#' @inheritParams sum_KM
#' @param probs a vector of proportions of interest for the quantiles
#'
#' @return quantiles of the estimated Kaplan-Meier curve
#'
#' @keywords internal
#' @noRd
quantile_KM <- function(x, probs) {
rslt <- length(probs)
for (i in 1:length(probs)) {
p <- 1 - probs[i]
j <- abs(x$S - p) < 1e-15 & x$events > 0
if (any(j)) {
if (abs(p - min(x$S)) < 1e-15) {
rslt[i] <- (x$t[j] + max(x$t))/2
}
else {
rslt[i] <- (x$t[j] + min(x$t[x$t > x$t[j] & x$events > 0]))/2
}
}
else {
j <- sum(x$S > p)
if (j == length(x$S) | p == 1) {
rslt[i] <- NA
}
else rslt[i] <- x$t[j + 1]
}
}
rslt
}
#' Compute the restricted mean survival time
#'
#' @inheritParams sum_KM
#' @param restriction a value used for computing restricted means, standard deviations,
#' and geometric means with censored time-to-event data. The default value of
#' \code{Inf} will cause restrictions at the highest observation. Note that the
#' same value is used for all variables of class \code{Surv}.
#'
#' @return the restricted mean survival time
#' @keywords internal
#' @noRd
mean_KM <- function(x, restriction = Inf) {
if (length(restriction) == 1)
restriction <- c(x$t[1] - 1, restriction)
if (restriction[2] == Inf)
restriction[2] <- x$t[length(x$t)]
tms <- c(restriction[1], x$t[x$t > restriction[1] & x$t < restriction[2]],
restriction[2])
s <- sum_KM(x, restriction)
s <- c(s[1], x$S[x$t > restriction[1] & x$t < restriction[2]], s[2])
ne <- tms <= 0
po <- tms >= 0
neS <- 1 - s[ne]
neX <- abs(c(diff(tms[ne]), 0))
neI <- neS != 0 & neX != 0
if (sum(neI) > 0)
rslt <- -sum(neS[neI] * neX[neI])
else rslt <- 0
poS <- s[po]
poX <- c(diff(tms[po]), 0)
poI <- poS != 0 & poX != 0
if (sum(poI) > 0)
rslt <- rslt + sum(poS[poI] * poX[poI])
attr(rslt, "restriction") <- restriction
rslt
}
# ------------------------------------------------------------------------------
# Compute descriptive statistics for different object classes
# ------------------------------------------------------------------------------
#' Descriptive Statistics for a Vector of Data
#' @param x a vector of data
#' @param probs a vector of proportions to use in describing (e.g., 0.25, 0.5)
#' @param thresholds a vector of thresholds used to dichotomize variables.
#' @param geometricMean logical, should we compute the geomtric mean? Defaults
#' to \code{FALSE}.
#' @param geomInclude if not \code{FALSE}, includes the geometric mean in the
#' descriptive statistics. Default is \code{FALSE}.
#' @param replaceZeroes if not \code{FALSE}, this indicates a value to be used
#' in place of zeroes when computing a geometric mean. If \code{TRUE}, a value
#' equal to one-half the lowest nonzero value is used. If a numeric value is
#' supplied, that value is used for all variables.
#'
#' @return A matrix of descriptive statistics
#'
#' @keywords internal
#' @noRd
describe_vector <- function(x, probs = c(0.25, 0.5, 0.75), thresholds = NULL,
geometricMean = FALSE, geomInclude = FALSE,
replaceZeroes = FALSE) {
if (is.character(x)) {
x <- as.factor(x)
}
if (is.factor(x) | is.logical(x)) {
x <- as.numeric(x)
}
if (!geomInclude) {
geometricMean <- geomInclude
}
ntholds <- ifelse(is.null(thresholds), 0, dim(thresholds)[1])
probs <- sort(unique(c(probs, 0, 1)))
# add sample size
rslt <- length(x)
if (rslt == 0) {
rslt <- c(rslt, rep(NaN, 7 + length(probs) + ntholds))
}
else {
# add number missing
u <- is.na(x)
rslt <- c(rslt, sum(u))
# only look at non-missing observations for remaining summaries
x <- x[!u]
if (length(x) == 0 | is.character(x)) {
if (!geomInclude) {
rslt <- c(rslt, rep(NA, 6 + length(probs) + ntholds))
} else {
rslt <- c(rslt, rep(NA, 7 + length(probs) + ntholds))
}
} else {
if (geomInclude) {
# add mean, sd, geometric mean, quantiles
rslt <- c(rslt, mean(x), stats::sd(x), ifelse1(
geometricMean,
exp(mean(log(ifelse(x == 0, replaceZeroes, x)))),
NA
), stats::quantile(x, probs))
} else {
# add mean, sd, quantiles
rslt <- c(rslt, mean(x), stats::sd(x), stats::quantile(x, probs))
}
if (ntholds > 0) {
# add thresholds if any are specified
for (j in 1:ntholds) {
u <- ifelse1(
thresholds[j, 1] == 0, x > thresholds[j, 2], x >= thresholds[j, 2]
) & ifelse1(
thresholds[j, 3] == 0, x < thresholds[j, 4], x <= thresholds[j, 4]
)
rslt <- c(rslt, mean(u))
}
}
# add in firstEvent and lastEvent
rslt <- c(rslt, Inf, NA, NA)
}
}
if (length(x) > 0) {
# add in a 0 if the vector is not a date
rslt <- matrix(c(rslt, 0), 1)
}
else {
rslt <- matrix(rslt, 1)
}
# set quantile names
qnames <- paste(format(100 * probs), "%", sep = "")
qnames[probs == 0.5] <- " Mdn"
qnames[probs == 0] <- " Min"
qnames[probs == 1] <- " Max"
tnames <- NULL
# set threshold column names
if (ntholds > 0) {
tholds <- thresholds
tholds[tholds == Inf | tholds == -Inf] <- 0
tnames <- paste(
sep = "", "Pr", ifelse(
thresholds[, 2] == -Inf,
paste(sep = "", ifelse(thresholds[, 3] == 0, "<", "<="), format(tholds[, 4])),
ifelse(
thresholds[, 4] == Inf,
paste(sep = "", ifelse(thresholds[, 1] == 0, ">", ">="), format(tholds[, 2])),
paste(
sep = "", ifelse(thresholds[, 1] == 0, "(", "["),
format(tholds[, 2]), ",", format(tholds[, 4]),
ifelse(thresholds[, 3] == 0, ")", "]")
)
)
)
)
}
if (geomInclude) {
dimnames(rslt) <- list("", c("N", "Msng", "Mean",
"Std Dev", "Geom Mn", qnames, tnames, "restriction",
"firstEvent", "lastEvent", "isDate"))
}
else {
dimnames(rslt) <- list("", c("N", "Msng", "Mean",
"Std Dev", qnames, tnames, "restriction", "firstEvent",
"lastEvent", "isDate"))
}
rslt
}
#' Describe a Survival variable
#'
#' @param x the variable of interest
#' @param resctriction a value used for computing restricted means,
#' standard deviations, and geometric means with censored time to event data.
#' The default value of \code{Inf} will cause restrictions at the highest
#' observation. Note that the same value is used for all variables of class \code{Surv}.
#' @inheritParams describe_vector
#'
#' @return A matrix with descriptive statistics for the Surv object
#' @keywords internal
#' @noRd
describe_surv <- function(x, probs = c(0.25, 0.5, 0.75), thresholds = NULL,
geometricMean = FALSE, geomInclude = FALSE,
replaceZeroes = FALSE, restriction = Inf) {
if (!survival::is.Surv(x))
stop("x must be a Surv object")
ntholds <- if (is.null(thresholds))
0
else dim(thresholds)[1]
probs <- sort(unique(c(probs, 0, 1)))
rslt <- dim(x)[1]
if (rslt == 0) {
rslt <- c(rslt, rep(NaN, 7 + length(probs) + ntholds))
}
else {
u <- is.na(x)
rslt <- c(rslt, sum(u))
x <- x[!u]
if (dim(x)[1] == 0) {
rslt <- c(rslt, rep(NA, 6 + length(probs) + ntholds))
}
else {
z <- KM(x)
tmp1 <- mean_KM(z, restriction)
x2 <- x
x2[, 1] <- x2[, 1]^2
z2 <- KM(x2)
# compute standard deviation as \sqrt{E[X^2] - E[X]^2}
tmp2 <- sqrt(mean_KM(z2, restriction^2) - tmp1^2)
if (geometricMean) {
x2 <- x
x2[, 1] <- ifelse(x2[, 1] == 0, log(replaceZeroes),
log(x2[, 1]))
z2 <- KM(x2)
tmp3 <- exp(mean_KM(z2, log(restriction)))
}
else tmp3 <- NA
if (any(x[, 2] == 1)) {
firstEvent <- min(x[x[, 2] == 1, 1])
lastEvent <- max(x[x[, 2] == 1, 1])
}
else {
firstEvent <- Inf
lastEvent <- -Inf
}
if (geomInclude) {
rslt <- c(rslt, tmp1, tmp2, tmp3, min(x[, 1]),
quantile_KM(z, probs[-c(1, length(probs))]), max(x[,
1]))
}
else {
rslt <- c(rslt, tmp1, tmp2, min(x[, 1]), quantile_KM(z,
probs[-c(1, length(probs))]), max(x[, 1]))
}
if (ntholds > 0) {
for (j in 1:ntholds) {
rslt <- c(rslt, ifelse1(thresholds[j, 1] ==
0, sum_KM(z, thresholds[j, 2]), sum_KM(z, thresholds[j,
2], FALSE)) - ifelse1(thresholds[j, 4] == Inf,
0, ifelse1(thresholds[j, 3] == 0, sum_KM(z,
thresholds[j, 4], FALSE), sum_KM(z, thresholds[j,
4]))))
}
}
rslt <- c(rslt, attr(tmp1, "restriction")[2],
firstEvent, lastEvent)
}
}
rslt <- matrix(c(rslt, 0), 1)
qnames <- paste(format(100 * probs), "%", sep = "")
qnames[probs == 0.5] <- " Mdn"
qnames[probs == 0] <- " Min"
qnames[probs == 1] <- " Max"
tnames <- NULL
if (ntholds > 0) {
tholds <- thresholds
tholds[tholds == Inf | tholds == -Inf] <- 0
tnames <- paste(sep = "", "Pr", ifelse(thresholds[,
2] == -Inf, paste(sep = "", ifelse(thresholds[,
3] == 0, "<", "<="), format(tholds[, 4])), ifelse(thresholds[,
4] == Inf, paste(sep = "", ifelse(thresholds[,
1] == 0, ">", ">="), format(tholds[, 2])), paste(sep = "",
ifelse(thresholds[, 1] == 0, "(", "["), format(tholds[,
2]), ",", format(tholds[, 4]), ifelse(thresholds[,
3] == 0, ")", "]")))))
}
if (geomInclude) {
dimnames(rslt) <- list("", c("N", "Msng", "Mean",
"Std Dev", "Geom Mn", qnames, tnames, "restriction",
"firstEvent", "lastEvent", "isDate"))
}
else {
dimnames(rslt) <- list("", c("N", "Msng", "Mean",
"Std Dev", qnames, tnames, "restriction", "firstEvent",
"lastEvent", "isDate"))
}
rslt
}
#' Describe a vector variable within strata
#'
#' @param x the vector of data to describe
#' @param strata the strata
#' @param subset a vector indicating a subset within which descriptive statistics
#' are desired.
#' @param probs a vector of proportions to use for quantiles
#' @param thresholds a vector of thresholds used to dichotomize variables.
#' @param geomInclude if not \code{FALSE} (the default), includes the geometric mean
#' in the descriptive statistics.
#' @param replaceZeroes if not \code{FALSE} (the default), this indicates a value
#' to be used in place of zeroes when computing a geometric mean. If \code{TRUE},
#' a value equal to one-half the lowest nonzero value is used. If a numeric value
#' is supplied, that value is used for all variables.
#'
#' @return the descriptive statistics within strata
#'
#' @keywords internal
#' @noRd
describe_stratified_vector <- function(x, strata, subset,
probs = c(0.25, 0.5, 0.75),
thresholds = NULL, geomInclude = FALSE,
replaceZeroes = FALSE) {
if (is.null(subset))
subset <- rep(TRUE, length(x))
if (length(x) != length(subset))
stop("length of variables must match length of subset")
if (is.null(strata))
strata <- rep(1, length(x))
if (length(x) != length(strata))
stop("length of variables must match length of strata")
x <- x[subset]
if (is.factor(x) | all(x[!is.na(x)] %in% c(0, 1))) {
geometricMean <- FALSE
} else {
geometricMean <- !any(x[!is.na(x)] < 0)
}
if (is.logical(replaceZeroes)) {
if (!replaceZeroes | is.factor(x) | all(x[!is.na(x)] %in%
c(0, 1))) {
replaceZeroes <- NA
} else {
replaceZeroes <- min(x[!is.na(x) & x > 0])/2
}
}
strata <- strata[subset]
s <- sort(unique(strata))
rslt <- describe_vector(x, probs, thresholds, geometricMean,
geomInclude, replaceZeroes)
if (length(s) > 1) {
for (i in s) rslt <- rbind(rslt, describe_vector(x[strata ==
i & !is.na(strata)], probs, thresholds, geometricMean,
geomInclude, replaceZeroes))
if (any(is.na(strata))) {
rslt <- rbind(rslt, describe_vector(x[is.na(strata)],
probs, thresholds, geometricMean, geomInclude,
replaceZeroes))
dimnames(rslt)[[1]] <- format(c("All", paste(" Str",
format(c(format(s), "NA")))))
}
else dimnames(rslt)[[1]] <- format(c("All", paste(" Str",
format(s))))
}
rslt
}
#' Describe a Date object within strata
#'
#' @inheritParams describe_stratified_vector
#'
#' @return descriptive statistics within strata for a Date object
#'
#' @keywords internal
#' @noRd
describe_stratified_date <- function(x, strata, subset,
probs = c(0.25, 0.5, 0.75),
thresholds = NULL, geomInclude = FALSE,
replaceZeroes = FALSE) {
if (!is.Date(x))
stop("x must be a Date object")
xi <- as.integer(x)
rslt <- describe_stratified_vector(xi, strata, subset, probs, thresholds,
geomInclude, replaceZeroes)
rslt[, "isDate"] <- 1
rslt
}
#' Describe a stratified Surv object
#'
#' @inheritParams describe_stratified_vector
#' @inheritParams describe_surv
#'
#' @return descriptive statistics within strata for a Surv object
#'
#' @keywords internal
#' @noRd
describe_stratified_surv <- function(x, strata, subset,
probs = c(0.25, 0.5, 0.75),
thresholds = NULL, geomInclude = FALSE,
replaceZeroes = FALSE, restriction = Inf) {
if (!survival::is.Surv(x))
stop("x must be a Surv object")
n <- dim(x)[1]
if (is.null(subset))
subset <- rep(TRUE, n)
if (n != length(subset))
stop("length of variables must match length of subset")
if (is.null(strata))
strata <- rep(1, n)
if (n != length(strata))
stop("length of variables must match length of strata")
x <- x[subset]
if (geomInclude) {
geometricMean <- !any(x[!is.na(x), 1] < 0)
}
else {
geometricMean <- FALSE
}
if (is.logical(replaceZeroes)) {
if (replaceZeroes) {
replaceZeroes <- min(x[!is.na(x) & x[, 1] > 0, 1])/2
} else {
replaceZeroes <- NA
}
}
strata <- strata[subset]
s <- sort(unique(strata))
rslt <- describe_surv(x, probs, thresholds, geomInclude, geometricMean,
replaceZeroes, restriction)
if (length(s) > 1) {
for (i in s) {
rslt <- rbind(rslt, describe_surv(x[strata == i & !is.na(strata)],
probs, thresholds, geomInclude,
geometricMean, replaceZeroes, restriction))
}
if (any(is.na(strata))) {
rslt <- rbind(rslt, describe_surv(x[is.na(strata)], probs,
thresholds, geomInclude, geometricMean,
replaceZeroes, restriction))
dimnames(rslt)[[1]] <- format(c("All",
paste(" Str", format(c(format(s), "NA")))))
} else {
dimnames(rslt)[[1]] <- format(c("All", paste(" Str", format(s))))
}
}
rslt
}
#' Describe a matrix object within strata
#'
#' @inheritParams describe_stratified_vector
#'
#' @return descriptive statistics within strata for a matrix object
#'
#' @keywords internal
#' @noRd
describe_stratified_matrix <- function(x, strata, subset,
probs = c(0.25, 0.5, 0.75),
thresholds = NULL, geomInclude = FALSE,
replaceZeroes = FALSE) {
if (!is.matrix(x)) {
stop("x must be a matrix")
}
p <- dim(x)[2]
nms <- dimnames(x)[[2]]
if (is.null(nms)) {
nms <- paste("V", 1:p, sep = "")
}
rslt <- NULL
for (i in 1:p) {
rslt <- rbind(
rslt,
describe_stratified_vector(x[, i], strata, subset, probs,
thresholds, geomInclude, replaceZeroes)
)
}
dimnames(rslt)[[1]] <- paste(format(rep(nms, each = (dim(rslt)[1])/p)), dimnames(rslt)[[1]])
rslt
}
#' Describe a list object within strata
#'
#' @inheritParams describe_stratified_surv
#'
#' @return descriptive statistics for each element of the list within strata
#'
#' @keywords internal
#' @noRd
describe_stratified_list <- function(x, strata, subset,
probs = c(0.25, 0.5, 0.75),
thresholds = NULL, geomInclude = FALSE,
replaceZeroes = FALSE, restriction = Inf) {
if (!is.list(x)) {
stop("x must be a list")
}
p <- length(x)
nms <- names(x)
if (is.null(nms)) {
nms <- paste("V", 1:p, sep = "")
}
rslt <- NULL
for (i in 1:p) {
if (survival::is.Surv(x[[i]])) {
rslt <- rbind(rslt, describe_stratified_surv(x[[i]], strata,
subset, probs, thresholds,
geomInclude, replaceZeroes,
restriction))
} else if (is.Date(x[[i]])) {
rslt <- rbind(rslt, describe_stratified_date(x[[i]], strata, subset,
probs, thresholds,
geomInclude, replaceZeroes))
} else {
rslt <- rbind(rslt, describe_stratified_vector(x[[i]], strata, subset,
probs, thresholds,
geomInclude, replaceZeroes))
}
}
dimnames(rslt)[[1]] <- paste(format(rep(nms, each = (dim(rslt)[1])/p)), dimnames(rslt)[[1]])
rslt
}
#' Print method for class uDescriptives
#' @noRd
#' @export
print.uDescriptives <- function (x, ..., sigfigs=max(3,getOption("digits")-3),width=9,nonsci.limit=5, print.it= TRUE) {
#
# prints CI or exp(CI) to specified sigfigs
# rounds exp(Est) to same number of decimal figures as exp(CI)
# prints Std Err or Robust SE to specified sigfigs
# rounds Estimate to same number of decimal figures as StdErr or Robust SE
# prints F stat to specified sigfigs
# prints P value to number of decimal figures as nonsci.limit unless < 10^-nonsci.limit when "< .00001" used
# centers all but df and P value, which is right justified
cmptRoundDigits <- function (x, sf) {
y <- max(abs(x),na.rm=TRUE)
if (y==0) {
sf
} else {
y <- trunc(log(y) / log(10)) - (y < 1)
max(0,sf - y - (y < sf))
}
}
frmtCol <- function (x, sf, nonsci.limit, colwidth=9, append="") {
rslt <- NULL
for (i in 1:length(x)) {
if (is.na(x[i])) {
tmp <- "NA"
} else {
rd <- cmptRoundDigits (x[i], sf)
if (rd <= nonsci.limit & abs(x[i]) < 10^nonsci.limit) {
tmp <- format(round(x[i],rd),nsmall=rd,width=1)
} else {
tmp <- format(round(x[i],rd), digits=sf, scientific=TRUE, width=1)
}
}
rslt <- c(rslt,ifelse(x[i]<0,tmp,paste(" ",tmp,sep="")))
}
rslt <- paste(rslt,append,sep="")
format(rslt, justify="centre", width=colwidth)
}
ncol <- dim(x)[2]
meancol <- (1:ncol)[dimnames(x)[[2]]=="Mean"]
mincol <- (1:ncol)[dimnames(x)[[2]]==" Min"]
maxcol <- (1:ncol)[dimnames(x)[[2]]==" Max"]
censMin <- x[,"firstEvent"] > x[,mincol]
censMin[is.na(censMin)] <- FALSE
censMax <- x[,"lastEvent"] < x[,maxcol]
censMax[is.na(censMax)] <- FALSE
if (!any(censMax)) {
restriction <- NULL
} else {
restriction <- frmtCol(x[,"restriction"],sigfigs,nonsci.limit,1,")")
restriction <- paste("(R",restriction,sep="")
restriction[!censMax] <- ""
restriction <- format(restriction, justify="left")
}
frmtCoefficients <- format(x[,1:(ncol-4),drop=FALSE])
for (j in 1:2) frmtCoefficients[,j] <- format (x[,j],justify="right",width=5)
frmtCoefficients[,mincol] <- frmtCol (x[,mincol],sigfigs,nonsci.limit,width,ifelse(censMin,"+",""))
for (j in 3:5) frmtCoefficients[,j] <- frmtCol (x[,j],sigfigs,nonsci.limit,width,ifelse(censMax,"+",""))
if(any(dimnames(x)[[2]] == "Geom Mn")){
indx <- 7:(ncol-4)
} else {
indx <- 6:(ncol-4)
}
indx <- indx[indx != maxcol]
for (j in indx) frmtCoefficients[,j] <- frmtCol (x[,j],sigfigs,nonsci.limit,width)
frmtCoefficients[,maxcol] <- frmtCol (x[,maxcol],sigfigs,nonsci.limit,width,ifelse(censMax,"+",""))
## set a temp variable to check if any are NA
dateBool <- any(is.na(x[,"isDate"]))
if(dateBool){
} else {
if (any(x[,"isDate"]==1)) {
xformCol <- c(3,5:(dim(x)[2]-4))
for (j in 1:length(xformCol)) {
if (substring(dimnames(x)[[2]][xformCol[j]],1,2)=="Pr") xformCol[j] <- NA
}
xformCol <- xformCol[!is.na(xformCol)]
orgn <- "1970-01-01"
frmtCoefficients[x[,"isDate"]==1,xformCol] <- format(as.Date(x[x[,"isDate"]==1,xformCol],orgn))
}
}
if (!is.null(restriction)) frmtCoefficients <- cbind(frmtCoefficients[,1:2,drop=FALSE],
"Restrict"=restriction,frmtCoefficients[,-(1:2),drop=FALSE])
if(print.it) print(frmtCoefficients,quote=FALSE)
invisible(frmtCoefficients)
}
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