Nothing
Qanova <- function(formula, data, q = 0.5, nboot = 600, ...){
#
# Test global hypothesis that J independent groups
# have equal medians.
# Performs well when there are tied values.
#
# Basically, use pbadepth in conjunction with the Harrell--Davis
# estimator.
#
#
if (missing(data)) {
mf <- model.frame(formula)
} else {
mf <- model.frame(formula, data)
}
cl <- match.call()
x <- split(model.extract(mf, "response"), mf[,2])
op=3
MC <- FALSE
chkcar=NA
for(j in 1:length(x))chkcar[j]=length(unique(x[[j]]))
#if(min(chkcar<20)) warning("Cardinality of sample space is less than 20 for one more groups. Type I error might not be controlled!")
nc <- length(pbadepth1(x, est = hd, q = q[1], allp = TRUE, SEED = FALSE, op = op, nboot = nboot, MC = MC, na.rm = TRUE)$psihat)
psimat <- matrix(NA, length(q), nc)
pvals <- NA
for (i in 1:length(q)) {
output <- pbadepth1(x, est = hd, q = q[i], allp = TRUE, SEED = FALSE, op = op, nboot = nboot, MC = MC, na.rm = TRUE)
psimat[i,] <- output$psihat
pvals[i] <- output$p.value
}
psidf <- as.data.frame(psimat)
rownames(psidf) <- paste0("q = ",q)
colnames(psidf) <- paste0("con", 1:nc)
pvalues <- as.data.frame(cbind(pvals, p.adjust(pvals, method = 'hochberg')))
rownames(pvalues) <- paste0("q = ",q)
colnames(pvalues) <- c("p-value", "p-adj")
result <- list(psihat = psidf, p.value = pvalues, contrasts = output$con, call = cl)
class(result) <- "qanova"
result
}
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