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# the following function is adapted from a suggestion by Robert Muenchen
# uses rcorr in the Hmisc package
# last modified 2014-09-04 by J. Fox
rcorr.adjust <- function (x, type = c("pearson", "spearman"),
use = c("complete.obs", "pairwise.complete.obs")) {
opt <- options(scipen = 5)
on.exit(options(opt))
type <- match.arg(type)
use <- match.arg(use)
x <- if (use == "complete.obs")
as.matrix(na.omit(x))
else as.matrix(x)
R <- rcorr(x, type = type)
P <- P.unadj <- R$P
p <- P[lower.tri(P)]
adj.p <- p.adjust(p, method = "holm")
P[lower.tri(P)] <- adj.p
P[upper.tri(P)] <- 0
P <- P + t(P)
P <- ifelse(P < 1e-04, 0, P)
P <- format(round(P, 4))
diag(P) <- ""
P[c(grep("0.0000", P), grep("^ 0$", P))] <- "<.0001"
P[grep("0.000$", P)] <- "<.001"
P.unadj <- ifelse(P.unadj < 1e-04, 0, P.unadj)
P.unadj <- format(round(P.unadj, 4))
diag(P.unadj) <- ""
P.unadj[c(grep("0.0000$", P.unadj), grep("^ 0$", P.unadj))] <- "<.0001"
P.unadj[grep("0.000$", P.unadj)] <- "<.001"
result <- list(R = R, P = P, P.unadj = P.unadj, type = type)
class(result) <- "rcorr.adjust"
result
}
print.rcorr.adjust <- function(x, ...){
cat("\n", if (x$type == "pearson") "Pearson" else "Spearman", "correlations:\n")
print(round(x$R$r, 4))
cat("\n Number of observations: ")
n <- x$R$n
if (all(n[1] == n)) cat(n[1], "\n")
else{
cat("\n")
print(n)
}
cat("\n Pairwise two-sided p-values:\n")
print(x$P.unadj, quote=FALSE)
cat("\n Adjusted p-values (Holm's method)\n")
print(x$P, quote=FALSE)
}
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