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dunn_test.R
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dunn_test.R
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#' @include utilities.R t_test.R
NULL
#'Dunn's Test of Multiple Comparisons
#'
#'@description Performs Dunn's test for pairwise multiple comparisons of the
#' ranked data. The mean rank of the different groups is compared. Used for
#' post-hoc test following Kruskal-Wallis test.
#'
#' The default of the \code{rstatix::dunn_test()} function is to perform a
#' two-sided Dunn test like the well known commercial softwares, such as SPSS
#' and GraphPad. This is not the case for some other R packages
#' (\code{dunn.test} and \code{jamovi}), where the default is to perform
#' one-sided test. This discrepancy is documented at
#' \href{https://github.com/kassambara/rstatix/issues/50}{https://github.com/kassambara/rstatix/issues/50}.
#'
#'@inheritParams t_test
#'@return return a data frame with some of the following columns: \itemize{
#' \item \code{.y.}: the y (outcome) variable used in the test. \item
#' \code{group1,group2}: the compared groups in the pairwise tests. \item
#' \code{n1,n2}: Sample counts. \item \code{estimate}: mean ranks difference.
#' \item \code{estimate1, estimate2}: show the mean rank values of the two
#' groups, respectively. \item \code{statistic}: Test statistic (z-value) used
#' to compute the p-value. \item \code{p}: p-value. \item \code{p.adj}: the
#' adjusted p-value. \item \code{method}: the statistical test used to compare
#' groups. \item \code{p.signif, p.adj.signif}: the significance level of
#' p-values and adjusted p-values, respectively. }
#'
#' The \strong{returned object has an attribute called args}, which is a list
#' holding the test arguments.
#'@details DunnTest performs the post hoc pairwise multiple comparisons
#' procedure appropriate to follow up a Kruskal-Wallis test, which is a
#' non-parametric analog of the one-way ANOVA. The Wilcoxon rank sum test,
#' itself a non-parametric analog of the unpaired t-test, is possibly
#' intuitive, but inappropriate as a post hoc pairwise test, because (1) it
#' fails to retain the dependent ranking that produced the Kruskal-Wallis test
#' statistic, and (2) it does not incorporate the pooled variance estimate
#' implied by the null hypothesis of the Kruskal-Wallis test.
#'
#'@references Dunn, O. J. (1964) Multiple comparisons using rank sums
#' Technometrics, 6(3):241-252.
#' @examples
#' # Simple test
#' ToothGrowth %>% dunn_test(len ~ dose)
#'
#' # Grouped data
#' ToothGrowth %>%
#' group_by(supp) %>%
#' dunn_test(len ~ dose)
#'@export
dunn_test <- function(data, formula, p.adjust.method = "holm", detailed = FALSE){
args <- as.list(environment()) %>%
.add_item(method = "dunn_test")
if(is_grouped_df(data)){
results <- data %>%
doo(.dunn_test, formula, p.adjust.method )
}
else{
results <- .dunn_test(data, formula, p.adjust.method)
}
if(!detailed){
results <- results %>%
select(-.data$method, -.data$estimate, -.data$estimate1, -.data$estimate2)
}
results %>%
set_attrs(args = args) %>%
add_class(c("rstatix_test", "dunn_test"))
}
.dunn_test <- function(data, formula, p.adjust.method = "holm"){
outcome <- get_formula_left_hand_side(formula)
group <- get_formula_right_hand_side(formula)
number.of.groups <- guess_number_of_groups(data, group)
if(number.of.groups == 1){
stop("all observations are in the same group")
}
data <- data %>%
select(!!!syms(c(outcome, group))) %>%
get_complete_cases() %>%
.as_factor(group)
x <- data %>% pull(!!outcome)
g <- data %>% pull(!!group)
group.size <- data %>% get_group_size(group)
if (!all(is.finite(g)))
stop("all group levels must be finite")
x.rank <- rank(x)
mean.ranks <- tapply(x.rank, g, mean, na.rm=TRUE)
grp.sizes <- tapply(x, g, length)
n <- length(x)
C <- get_ties(x.rank, n)
compare.meanrank <- function(i, j){
mean.ranks[i] - mean.ranks[j]
}
compare.stats <- function(i,j) {
dif <- mean.ranks[i] - mean.ranks[j]
A <- n * (n+1) / 12
B <- (1 / grp.sizes[i] + 1 / grp.sizes[j])
zval <- dif / sqrt((A - C) * B)
zval
}
compare.levels <- function(i, j) {
dif <- abs(mean.ranks[i] - mean.ranks[j])
A <- n * (n+1) / 12
B <- (1 / grp.sizes[i] + 1 / grp.sizes[j])
zval <- dif / sqrt((A - C) * B)
pval <- 2 * stats::pnorm(abs(zval), lower.tail = FALSE)
pval
}
ESTIMATE <- stats::pairwise.table(
compare.meanrank, levels(g),
p.adjust.method = "none"
) %>% tidy_squared_matrix("diff")
PSTAT <- stats::pairwise.table(
compare.stats, levels(g),
p.adjust.method = "none"
) %>% tidy_squared_matrix("statistic")
PVAL <- stats::pairwise.table(
compare.levels, levels(g),
p.adjust.method = "none"
) %>%
tidy_squared_matrix("p") %>%
mutate(method = "Dunn Test", .y. = outcome) %>%
adjust_pvalue(method = p.adjust.method) %>%
add_significance("p.adj") %>%
add_column(statistic = PSTAT$statistic, .before = "p") %>%
add_column(estimate = ESTIMATE$diff, .before = "group1") %>%
select(.data$.y., .data$group1, .data$group2, .data$estimate, everything())
n1 <- group.size[PVAL$group1]
n2 <- group.size[PVAL$group2]
mean.ranks1 <- mean.ranks[PVAL$group1]
mean.ranks2 <- mean.ranks[PVAL$group2]
PVAL %>%
add_column(n1 = n1, n2 = n2, .after = "group2") %>%
add_column(estimate1 = mean.ranks1, estimate2 = mean.ranks2, .after = "estimate")
}
get_ties <- function(x, n) {
x.sorted <- sort(x)
pos <- 1
tiesum <- 0
while (pos <= n) {
val <- x.sorted[pos]
nt <- length(x.sorted[x.sorted == val])
pos <- pos + nt
if (nt > 1){
tiesum <- tiesum + nt^3 - nt
}
}
tiesum / (12 * (n - 1))
}