genericcorrplot: Correlation plot for data

In ShouyeLiu/metaboliteUtility:

Description

Correlation plot for data

Usage

genericcorrplot(pearsoncorr, title = "Correlation map", method = "circle",
  type = "upper", add = FALSE,
  col = (grDevices::colorRampPalette(c("blue", "white", "red")))(200),
  bg = "white", is.corr = TRUE, diag = FALSE, outline = TRUE,
  oma = c(0, 0, 0, 0), mar = c(0, 0, 2, 0), addgrid.col = NULL,
  addCoef.col = NULL, addCoefasPercent = FALSE, order = "original",
  tl.pos = "td", tl.cex = 0.75, tl.col = "black", tl.offset = 0.4,
  tl.srt = 55, cl.pos = NULL, cl.lim = NULL, cl.length = NULL,
  cl.cex = 0.8, cl.ratio = 0.15, cl.align.text = "c", cl.offset = 0.5,
  number.cex = 0.7, addshade = c("negative", "positive", "all"),
  shade.lwd = 1, shade.col = "white", p.mat = pearsoncorr$P,
  sig.level = 0.05, insig = "blank", pch = 6, pch.col = "black",
  pch.cex = 2, plotCI = "n", ...)

Arguments

pearsoncorr

person correlation matrix

Details

Please see corrplot

Examples

 library(Hmisc)
 data(corrplot_data)
 tmpDataset <- as.matrix(corrplot_data);
 tmpPearsonCorr <- Hmisc::rcorr(tmpDataset,type="pearson");
 tmpPearsonMat <- tmpPearsonCorr$r;
 diag(tmpPearsonMat) <- 0;
 tmpPearsonMat[lower.tri(tmpPearsonMat)] <- 0;
 tmpCorrplot <- genericcorrplot(tmpPearsonCorr,title="Pearson correlation analysis");

ShouyeLiu/metaboliteUtility documentation built on May 6, 2019, 9:07 a.m.