Nothing
qn.test <-
function (..., data = NULL,
test = c("KW","vdW","NS"),
method=c("asymptotic","simulated","exact"),
dist=FALSE,Nsim=10000)
{
#############################################################################
# This function "qn.test" tests whether k samples (k>1) come from a common
# continuous distribution, using the QN rank test. See Lehmann (2006),
# Nonparametrics, Statistical Methods Based on Ranks, Appendix Corollary 10.
# Ties are handled by using average rank scores.
# While the asymptotic P-value is always returned, there is the option
# to get an estimate based on Nsim simulations or an exact value based
# on the full enumeration distribution, provided method = "exact" is chosen
# and the number of full enumerations is <= the Nsim specified.
# If the latter is not the case, simulation is used with the indicated Nsim.
# These simulated or exact P-values are appropriate under the continuity
# assumption or, when ties are present, they are still appropriate
# conditionally on the tied rank pattern, provided randomization took
# place in allocating subjects to the respective samples, i.e., also
# under random sampling from a common discrete parent population.
#
#
#
# Inputs:
# ...: can either be a sequence of k (>1) sample vectors,
#
# or a list of k (>1) sample vectors,
#
# or y, g, where y contains the concatenated
# samples and g is a factor which by its levels
# identifies the samples in y,
#
# or a formula y ~ g with y and g as in previous case.
#
# test: specifies the ranks scores to be used, averaging the scores
# of tied observations.
# test = "KW" uses scores 1:N ( ==> Kruskal-Wallis test)
# test = "vdW" uses the van der Waerden scores qnorm(1:N/(N+1))
# test = "NS" uses normal scores, expected standard normal order
# statistics, uses function normOrder of package SuppDists.
# Other scores could easily be added to this function.
#
# method: takes values "asymptotic", "simulated", or "exact".
# The value "asymptotic" causes calculation of P-values
# using the asymptotic chi-square approximation, always done.
#
# The value "simulated" causes estimation of P-values
# by randomly splitting the the pooled data into
# samples of sizes ns[1], ..., ns[k], where
# ns[i] is the size of the i-th sample vector,
# and n = ns[1] + ... + ns[k] is the pooled sample size.
# For each such random split the QN statistic is
# computed. This is repeated Nsim times and the proportions
# of simulated values >= the actually observed QN value
# is reported as P-value estimate.
#
# The value "exact" enumerates all n!/(ns[1]! * ... * ns[k])
# splits of the pooled sample and computes the QN statistic.
# The proportion of all enumerated QN statistics
# that are >= the actually observed QN value
# is reported as exact (conditional) P-value.
#
# dist: = FALSE (default) or TRUE, TRUE causes the simulated
# or fully enumerated vector of the QN statstic to be returned
# as null.dist.
#
# Nsim: number of simulations to perform,
# for method = "exact" to take hold, it needs to be at least
# equal the number of all possible splits of the pooled
# data into samples of sizes ns[1], ..., ns[k], where
# ns[i] is the size of the i-th sample vector.
#
# When there are NA's among the sample values they are removed,
# with a warning message indicating the number of NA's.
# It is up to the user to judge whether such removals make sense.
#
# An example:
# z1 <- c(0.824, 0.216, 0.538, 0.685)
# z2 <- c(0.448, 0.348, 0.443, 0.722)
# z3 <- c(0.403, 0.268, 0.440, 0.087)
# qn.test(z1,z2,z3,method="exact",dist=T,Nsim=100000)
# or
# qn.test(list(z1,z2,z3),test="KW",method="exact",dist=T,Nsim=100000)
# which produces the output below.
#############################################################################
#
# Kruskal-Wallis k-sample test.
#
# Number of samples: 3
# Sample sizes: 4 4 4
# Total number of values: 12
# Number of unique values: 12
#
# Null Hypothesis: All samples come from a common population.
#
# QN asympt. P-value exact P-Value
# 3.5769231 0.1672172 0.1729870
#
#
# Warning: At least one sample size is less than 5.
# asymptotic p-values may not be very accurate.
#
#############################################################################
# In order to get the output list, call
# qn.out <- qn.test(list(z1,z2,z3),test="KW",method="exact",dist=T,Nsim=100000)
# then qn.out is of class ksamples and has components
# > names(qn.out)
# [1] "test.name" "k" "ns" "N" "n.ties" "qn"
# [7] "warning" "null.dist" "method" "Nsim"
#
# where
# test.name = "Kruskal-Wallis", "van der Waerden", or "normal scores"
# k = number of samples being compared
# ns = vector of the k sample sizes ns[1],...,ns[k]
# N = ns[1] + ... + ns[k] total sample size
# n.ties = number of ties in the combined set of all n observations
# qn = 2 (or 3) vector containing the QN statistics, its asymptotic P-value,
# (and its exact or simulated P-value).
# warning = logical indicator, warning = TRUE indicates that at least
# one of the sample sizes is < 5.
# null.dist is a vector of simulated values of the QN statistic
# or the full enumeration of such values.
# This vector is given when dist = TRUE is specified,
# otherwise null.dist = NULL is returned.
# method = one of the following values: "asymptotic", "simulated", "exact"
# as it was ultimately used.
# Nsim = number of simulations used, when applicable.
#
# The class ksamples causes qn.out to be printed in a special output
# format when invoked simply as: > qn.out
# An example was shown above.
#
# Fritz Scholz, August 2012
#
#################################################################################
ave.score <- function(z, scores){
# This function takes a data vector z and a vector scores
# of same length and returns a vector av.scores of scores
# using average scores for each group of tied
# observations in z. av.scores and scores have same length.
N <- length(z)
rz <- rank(z)
r.rz <- rank(z,ties.method="random")
rz.u <- unique(rz)
av.scores <- rep(0,N)
for(rz.ui in rz.u){
av.scores[rz==rz.ui] <- mean(scores[r.rz[rz
==rz.ui]])
}
av.scores
}
samples <- io(...,data = data)
test <- match.arg(test)
method <- match.arg(method)
out <- na.remove(samples)
na.t <- out$na.total
if( na.t > 1) print(paste("\n",na.t," NAs were removed!\n\n"))
if( na.t == 1) print(paste("\n",na.t," NA was removed!\n\n"))
samples <- out$x.new
k <- length(samples)
if (k < 2) stop("Must have at least two samples.")
ns <- sapply(samples, length)
n <- sum(ns)
if (any(ns == 0)) stop("One or more samples have no observations.")
x <- unlist(samples)
if(test == "KW"){ scores.vec <- 1:n }
if (test == "NS") {
if (!requireNamespace("SuppDists", quietly = TRUE)){
# if (!exists("normOrder")) library(SuppDists)
stop("SuppDists (>= 1.1-9.4) needed for this function to work. Please install it.",
call. = FALSE)
}
scores.vec <- normOrder(n)
}
if(test == "vdW") {
scores.vec <- qnorm((1:n)/(n + 1))
}
QNobs <- 0
pval <- 0
rx <- ave.score(x,scores.vec)
svar <- var(rx)
smean <- mean(rx)
L <- length(unique(rx))
if(dist == TRUE) Nsim <- min(Nsim,1e8)
ncomb <- 1
if( method == "exact"){
np <- n
for(i in 1:(k-1)){
ncomb <- ncomb * choose(np,ns[i])
np <- np-ns[i]
}
# it is possible that ncomb overflows to Inf
if(!(ncomb < Inf)) stop('ncomb = Inf, method = "exact" not possible\n')
}
if( method == "exact" & Nsim < ncomb) {
method <- "simulated"
}
if( method == "exact" & dist == TRUE ) nrow <- ncomb
if( method == "simulated" & dist == TRUE ) nrow <- Nsim
if( method == "simulated" ) ncomb <- 1 # don't need ncomb anymore
if(method == "asymptotic"){
Nsim <- 1
dist <- FALSE
}
useExact <- FALSE
if(method == "exact") useExact <- TRUE
if(dist==T){
QNvec <- numeric(nrow)
}else{
QNvec <- 0
}
out <- .C("QNtest", pval=as.double(pval),
Nsim=as.integer(Nsim), k=as.integer(k),
rx=as.double(rx), ns=as.integer(ns),
useExact=as.integer(useExact),
getQNdist=as.integer(dist),
ncomb=as.double(ncomb),QNobs=as.double(QNobs),
QNvec = as.double(QNvec), PACKAGE = "kSamples")
QNobs <- (out$QNobs - n*smean^2)/svar
pval <- out$pval
if(dist){
QNvec <- round((out$QNvec- n*smean^2)/svar,8)
}
pval.asympt <- 1-pchisq(QNobs,k-1)
if(method=="asymptotic"){
qn <- c(QNobs,pval.asympt)
}else{
qn <- c(QNobs,pval.asympt,pval)
}
if(method=="asymptotic"){
names(qn) <- c("test statistic"," asympt. P-value")
}
if(method=="exact"){
names(qn) <- c("test statistic"," asympt. P-value","exact P-Value")
}
if(method=="simulated"){
names(qn) <- c("test statistic"," asympt. P-value","sim. P-Value")
}
warning <- FALSE
if(min(ns) < 5) warning <- TRUE
if(dist == FALSE | method == "asymptotic") QNvec <- NULL
if(test == "vdW") test.name <- "van der Waerden scores"
if(test == "NS") test.name <- "normal scores"
if(test == "KW") test.name <- "Kruskal-Wallis"
object <- list(test.name = test.name,
k = k, ns = ns, N = n, n.ties = n - L,
qn = qn, warning = warning, null.dist = QNvec,
method=method, Nsim=Nsim)
class(object) <- "kSamples"
object
}
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