R/corstars.R

In Cogitos/statxp: Filter data, format and export statistical results

#' Compute a correlation matrix with stars to indicate significant correlations
#'  
#' A function to compute a correlation matrix with stars 
#'   to indicate significant correlations (p<.05 = *, 
#'   p<.01 = **, p<.001).
#' Return a data frame.
#' 
#' @author
#' Guillaume T. Vallet \email{gtvallet@@gmail.com}, University of de Montreal (Canada);
#' 
#' @param x A data frame with only the variables to correlate.
#' @return Return a data frame representing the correlation matrix.
#' @keywords correlation, matrix, significance
#' @export
corstars <- function(x){ 
  require(Hmisc) 
  x <- as.matrix(x) 
  R <- rcorr(x)$r 
  p <- rcorr(x)$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) 
}