# adapted from here
# https://github.com/kyuni22/ksmv/blob/master/functions/corstarsl.R
corstarsl <- function(x, type = c("pearson", "spearman"), digits = 2, full = FALSE)
{
type <- match.arg(type)
x <- as.matrix(x)
Rbkp <- R <- rcorr(x, type = type)$r
p <- rcorr(x, type = type)$P
## define notions for significance levels; spacing is important.
mystars <- ifelse(p < .001, "***",
ifelse(p < .01, "** ",
ifelse(p < .05, "* ", ifelse(p < .1, "+ ", " "))))
## trunctuate the matrix that holds the correlations to two decimal
R <- format(round(R, digits))
R <- apply(R, 2, str_replace, "0.", ".")
## build a new matrix that includes the correlations with their apropriate stars
Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x))
diag(Rnew) <- paste(diag(R), " ", sep="")
rownames(Rnew) <- colnames(x)
colnames(Rnew) <- paste(colnames(x), "", sep="")
## remove upper triangle
Rnew <- as.matrix(Rnew)
Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
Rnew <- as.data.frame(Rnew)
## remove last column and return the matrix (which is now a data frame)
Rnew <- cbind(Rnew[1:length(Rnew)-1])
if(full)
out <- list("Rformatted" = Rnew, "R" = Rbkp, "p" = p)
else
out <- Rnew
return(out)
}
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