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## bwsManyOneTest.R
##
## Copyright (C) 2017 Thorsten Pohlert
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 3 of the License, or
## (at your option) any later version.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## A copy of the GNU General Public License is available at
## http://www.r-project.org/Licenses/
##
#' @title BWS Many-To-One Comparison Test
#' @description Performs Baumgartner-Weiß-Schindler many-to-one comparison test.
#' @details
#' For many-to-one comparisons (pairwise comparisons with one control)
#' in an one-factorial layout with non-normally distributed
#' residuals Baumgartner-Weiß-Schindler's non-parametric test can be performed.
#' Let there be \eqn{k} groups including the control,
#' then the number of treatment levels is \eqn{m = k - 1}.
#' Then \eqn{m} pairwise comparisons can be performed between
#' the \eqn{i}-th treatment level and the control.
#' H\eqn{_i: F_0 = F_i} is tested in the two-tailed case against
#' A\eqn{_i: F_0 \ne F_i, ~~ (1 \le i \le m)}.
#'
#' This function is a wrapper function that sequentially
#' calls \code{\link[BWStest]{bws_stat}} and \code{\link[BWStest]{bws_cdf}}
#' for each pair. For the default test method (\code{"BWS"}) the original
#' Baumgartner-Weiß-Schindler test statistic B and its corresponding Pr(>|B|)
#' is calculated. For \code{method == "BWS"} only a two-sided test is possible.
#'
#' For \code{method == "Murakami"} the modified BWS statistic
#' denoted B* and its corresponding Pr(>|B*|) is computed by sequentially calling
#' \code{\link[BWStest]{murakami_stat}} and \code{\link[BWStest]{murakami_cdf}}.
#' For \code{method == "Murakami"} only a two-sided test is possible.
#'
#' If \code{alternative == "greater"} then the alternative, if one
#' population is stochastically larger than the other is tested:
#' H\eqn{_i: F_0 = F_i} against A\eqn{_i: F_0 \ge F_i, ~~ (1 \le i \le m)}.
#' The modified test-statistic B* according to Neuhäuser (2001) and its
#' corresponding Pr(>B*) or Pr(<B*) is computed by sequentally calling
#' \code{\link[BWStest]{murakami_stat}} and \code{\link[BWStest]{murakami_cdf}}
#' with \code{flavor = 2}.
#'
#' The p-values can be adjusted to account for Type I error
#' inflation using any method as implemented in \code{\link{p.adjust}}.
#'
#' @name bwsManyOneTest
#' @template class-PMCMR
#' @keywords htest nonparametric
#' @inherit cuzickTest note
#' @references
#' Baumgartner, W., Weiss, P., Schindler, H. (1998) A nonparametric test for the
#' general two-sample problem, \emph{Biometrics} \bold{54}, 1129--1135.
#'
#' Murakami, H. (2006) K-sample rank test based on modified Baumgartner statistic and its power
#' comparison, \emph{J Jpn Comp Statist} \bold{19}, 1--13.
#'
#' Neuhäuser, M. (2001) One-Side Two-Sample and Trend Tests Based on a Modified
#' Baumgartner-Weiss-Schindler Statistic. \emph{J Nonparametric Stat} \bold{13}, 729--739.
#'
#' @examples
#' out <- bwsManyOneTest(weight ~ group, PlantGrowth, p.adjust="holm")
#' summary(out)
#'
#' ## A two-sample test
#' set.seed(1245)
#' x <- c(rnorm(20), rnorm(20,0.3))
#' g <- gl(2, 20)
#' summary(bwsManyOneTest(x ~ g, alternative = "less", p.adjust="none"))
#' summary(bwsManyOneTest(x ~ g, alternative = "greater", p.adjust="none"))
#'
#' \dontrun{
#' ## Check with the implementation in package BWStest
#' BWStest::bws_test(x=x[g==1], y=x[g==2], alternative = "less")
#' BWStest::bws_test(x=x[g==1], y=x[g==2], alternative = "greater")
#' }
#' @seealso
#' \code{\link[BWStest]{murakami_stat}}, \code{\link[BWStest]{murakami_cdf}},
#' \code{\link[BWStest]{bws_stat}}, \code{\link[BWStest]{bws_cdf}}.
#' @export
bwsManyOneTest <- function(x, ...) UseMethod("bwsManyOneTest")
#' @rdname bwsManyOneTest
#' @method bwsManyOneTest default
#' @aliases bwsManyOneTest.default
#' @template one-way-parms
#' @param alternative the alternative hypothesis. Defaults to \code{two.sided}.
#' @param p.adjust.method method for adjusting p values (see \code{\link{p.adjust}}).
#' @param method a character string specifying the test statistic to use. Defaults to \code{BWS}.
#' @importFrom stats p.adjust
#' @importFrom stats p.adjust.methods
#' @importFrom stats pairwise.table
#' @importFrom stats complete.cases
#' @importFrom BWStest murakami_stat
#' @importFrom BWStest murakami_cdf
#' @importFrom BWStest bws_stat
#' @importFrom BWStest bws_cdf
#' @export
bwsManyOneTest.default <-
function(x, g, alternative = c("two.sided", "greater", "less"),
method = c("BWS", "Murakami", "Neuhauser"),
p.adjust.method = p.adjust.methods, ...)
{
if (is.list(x)) {
if (length(x) < 2L)
stop("'x' must be a list with at least 2 elements")
DNAME <- deparse(substitute(x))
x <- lapply(x, function(u) u <- u[complete.cases(u)])
k <- length(x)
l <- sapply(x, "length")
if (any(l == 0))
stop("all groups must contain data")
g <- factor(rep(1 : k, l))
#
if (is.null(x$p.adjust.method)){
p.adjust.method <- p.adjust.methods[1]
} else {
p.adjust.method <- x$p.adjust.method
}
method <- x$method
alternative <- x$alternative
x <- unlist(x)
}
else {
if (length(x) != length(g))
stop("'x' and 'g' must have the same length")
DNAME <- paste(deparse(substitute(x)), "and",
deparse(substitute(g)))
OK <- complete.cases(x, g)
x <- x[OK]
g <- g[OK]
if (!all(is.finite(g)))
stop("all group levels must be finite")
g <- factor(g)
k <- nlevels(g)
if (k < 2)
stop("all observations are in the same group")
}
N <- length(x)
if (N < 2)
stop("not enough observations")
p.adjust.method <- match.arg(p.adjust.method)
alternative <- match.arg(alternative)
method <- match.arg(method)
n <- tapply(x, g, length)
## Change method string
if (alternative != "two.sided"){
method <- "B2"
} else if (method == "Murakami"){
method <- "B1"
} else if (method == "Neuhauser"){
method <- "B1"
}
METHOD <- switch(method,
"BWS" = c("BWS Two-Sample Test",
" for multiple comparisons with one control"),
"B1" = c("Murakami's modified Two-Sample BWS Test",
" for multiple comparisons with one control"),
"B2" = c("Neuhauser's modified Two-Sample BWS Test",
" for multiple comparisons with one control"))
stat <- switch(method,
"BWS" = sapply(2:k, function(j)
do.call("bws_stat",
list(x = x[as.integer(g) == 1],
y = x[as.integer(g) == j])
)
),
"B1" = sapply(2:k, function(j)
do.call("murakami_stat",
list(x = x[as.integer(g) == 1],
y = x[as.integer(g) == j],
flavor = 1)
)
),
"B2" = sapply(2:k, function(j)
do.call("murakami_stat",
list(x = x[as.integer(g) == 1],
y = x[as.integer(g) == j],
flavor = 2)
)
)
)
## pval <- switch(method,
if (method == "BWS") {
pval <- do.call("bws_cdf",
list(b = stat, maxj = 3, lower=FALSE)
)
} else if (method == "B1"){
pval <- sapply(2:k, function(j)
do.call("murakami_cdf",
list(B = stat[j-1],
n1 = n[1],
n2 = n[j],
flavor = 1,
lower = FALSE)
)
)
} else if (alternative == "greater"){
pval <- sapply(2:k, function(j)
do.call("murakami_cdf",
list(B = stat[j-1],
n1 = n[j],
n2 = n[1],
flavor = 2,
lower=FALSE)
)
)
} else {
pval <- sapply(2:k, function(j)
do.call("murakami_cdf",
list(B = stat[j-1],
n1 = n[1],
n2 = n[j],
flavor = 2,
lower=TRUE)
)
)
}
## adjust p-values
pval <- p.adjust(pval, method = p.adjust.method)
## Create matrices
STAT <- cbind(stat)
colnames(STAT) <- levels(g)[1]
rownames(STAT) <- levels(g)[-1]
PVAL <- cbind(pval)
colnames(PVAL) <- colnames(STAT)
rownames(PVAL) <- rownames(STAT)
ans <- list(method = METHOD,
data.name = DNAME,
p.value = PVAL,
p.adjust.method=p.adjust.method,
dist = ifelse(method == "BWS", "B", "B*"),
statistic = STAT,
alternative = alternative,
model = data.frame(x=x, g=g))
class(ans) <- "PMCMR"
ans
}
#' @rdname bwsManyOneTest
#' @method bwsManyOneTest formula
#' @aliases bwsManyOneTest
#' @template one-way-formula
#' @export
bwsManyOneTest.formula <-
function(formula, data, subset, na.action,
alternative = c("two.sided", "greater", "less"),
method = c("BWS", "Murakami", "Neuhauser"),
p.adjust.method = p.adjust.methods, ...)
{
mf <- match.call(expand.dots=FALSE)
m <- match(c("formula", "data", "subset", "na.action"), names(mf), 0L)
mf <- mf[c(1L, m)]
mf[[1L]] <- quote(stats::model.frame)
if(missing(formula) || (length(formula) != 3L))
stop("'formula' missing or incorrect")
mf <- eval(mf, parent.frame())
if(length(mf) > 2L)
stop("'formula' should be of the form response ~ group")
DNAME <- paste(names(mf), collapse = " by ")
alternative <- match.arg(alternative)
p.adjust.method <- match.arg(p.adjust.method)
method = match.arg(method)
names(mf) <- NULL
y <- do.call("bwsManyOneTest",
c(as.list(mf), alternative = alternative,
p.adjust.method = p.adjust.method, method=method))
y$data.name <- DNAME
y
}
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