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#' Correlation Analysis
#'
#' @param data a dataframe or matrix
#' @param method a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default, 1), "kendall" (2), or "spearman" (3): can be abbreviated.
#' @return correlation analysis with significant star.
#' @export
#'
#' @examples
#' cor_star(mtcars)
cor_star<- function(data,method="pearson") {
if (method==1) method='pearson'
if (method==2) method='kendall'
if (method==3) method='spearman'
message('')
message(method,' method')
res.cor = round(cor(data),3)
for (i in 1:(nrow(res.cor)-1)) {
for (j in (i+1):nrow(res.cor)) {
sig = as.numeric(cor.test(data[,rownames(res.cor)[i]],
data[,rownames(res.cor)[j]],
method=method)$p.value)
if (sig <= 0.001) {
star = "***"
}
if (sig <= 0.01 & sig > 0.001) {
star = "** "
}
if (sig <= 0.05 & sig > 0.01) {
star = "* "
}
if (sig > 0.05) {
star = " "
}
res.cor[i,j]=paste0(res.cor[i,j],star)
}
res.cor[(i+1):nrow(res.cor),i]=''
}
for (i in 1:nrow(res.cor)) {
res.cor[i,i]=rownames(res.cor)[i]
}
message('')
message('***: p <=0.001')
message(' **: p 0.01 ~ 0.001')
message(' *: p 0.05 ~ 0.01')
message('')
rownames(res.cor)=NULL
colnames(res.cor)[1]=' '
as.data.frame(res.cor)
}
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