R/corstars.R

In juttaGa/jmisc: Contains useful summary/table/plot functions

#' corstars
#'
#' Calculate pairwise correlation coefficients and print with significance stars.
#'
#' @param x data frame for which pairwise correlations are calculated.
#' @param method either "pearson" (the default) or "spearman".
#' @param ... other parameters passed through.
#' @return Prints table with pairwise correlation coefficients and significance stars.
#' @details Significance code: *: p<0.05, **: p<0.01, ***: p<0.001.
#' @importFrom Hmisc rcorr
#' @export

corstars <- function(x, type=c("pearson", "spearman"), ...){

  x <- as.matrix(x)
  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, "* ", " ")))

  ## trunctuate the matrix that holds the correlations to two decimal
  R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]

  ## 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])
  return(Rnew)
}