A code snippet creating a pair correlation plot that works well to visualize correlations in large datasets such as MCMC chains or similar. Marginal distributions on the diagonal, correlation density plots on the lower triangle, spearman correlation in scaled numbers on the upper triangle.

CorrelationDensityPlotWithIpanel.r

library(IDPmisc)

panel.hist <- function(x, ...)
{
  usr <- par("usr"); on.exit(par(usr))
  par(usr = c(usr[1:2], 0, 1.5) )
  h <- hist(x, plot = FALSE)
  breaks <- h$breaks; nB <- length(breaks)
  y <- h$counts; y <- y/max(y)
  rect(breaks[-nB], 0, breaks[-1], y, col="blue4", ...)
}

panel.cor <- function(x, y, digits=2, prefix="", cex.cor)
{
  usr <- par("usr"); on.exit(par(usr))
  par(usr = c(0, 1, 0, 1))
  r <- abs(cor(x, y, method = "spearman"))
  txt <- format(c(r, 0.123456789), digits=digits)[1]
  txt <- paste(prefix, txt, sep="")
  if(missing(cex.cor)) cex <- 0.8/strwidth(txt)
  text(0.5, 0.5, txt, cex = cex * r)
}

betterPairs <- function(YourData){
  return(pairs(YourData, lower.panel=function(...) {par(new=TRUE);ipanel.smooth(...)}, diag.panel=panel.hist, upper.panel=panel.cor))
}

# Example

x = rnorm(10000)
betterPairs(data.frame(A = x, B = 0.6 * x + 0.3 * rnorm(10000), C = rnorm(10000)))