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Correlation table with significance indicators
corstar <- function(x, y = NULL, use = "pairwise", method = "pearson", round = 3, row.labels, col.labels, ...) {
require(psych)
ct <- corr.test(x, y, use, method) # calculate correlation
r <- ct$r # get correlation coefs
p <- ct$p # get p-values
stars <- ifelse(p < .001, "***", ifelse(p < .01, "** ", ifelse(p < .05, "* ", " "))) # generate significance stars
m <- matrix(NA, nrow = nrow(r) * 2, ncol = ncol(r) + 1) # create empty matrix
rlab <- if(missing(row.labels)) rownames(r) else row.labels # add row labels
clab <- if(missing(col.labels)) {
if(is.null(colnames(r)))
deparse(substitute(y))
else
colnames(r)
} else {
col.labels # add column labels
}
rows <- 1:nrow(m) # row indices
cols <- 2:ncol(m) # column indices
odd <- rows %% 2 == 1 # odd rows
even <- rows %% 2 == 0 # even rows
m[odd, 1] <- rlab # add variable names
m[even, 1] <- rep("", sum(even)) # add blank
m[odd, cols] <- paste(format(round(r, round), nsmall = round, ...), stars, sep = "") # add r coefs
m[even, cols] <- paste("(", format(round(p, round), nsmall = round, ...), ")", sep = "") # add p-values
colnames(m) <- c(" ", clab) # add colnames
m # return matrix
}
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