ggcorrplot.mixed: Visualize a correlation matrix using mixed methods

In caijun/ggcorrplot2: Visualize a Correlation Matrix using ggplot2

Visualize a correlation matrix using mixed methods

Description

Visualize a correlation matrix using mixed methods

Usage

ggcorrplot.mixed(
  corr,
  upper = c("circle", "square", "ellipse", "number"),
  lower = c("number", "square", "ellipse", "circle"),
  col = NULL,
  p.mat = NULL,
  sig.lvl = 0.05,
  number.digits = 2,
  insig = c("pch", "blank", "label_sig"),
  pch = 4,
  pch.cex = 5
)

Arguments

corr	a correlation matrix to be visualized
upper	a character indicating the visualization method of the upper triangular matrix to be used. Currently, it supports four methods, named "circle" (default), "square", "ellipse", "number".
lower	a character indicating the visualization method of the lower triangular matrix to be used. Currently, it supports four methods, named "circle", "square", "ellipse", "number"(default).
col	a vector of the colors to be used, which are distributed uniformly from -1 to 1. If NULL, col will be set to RColorBrewer::brewer.pal(n = 11, name = "RdBu"), the default colour scheme of corrplot.
p.mat	a matrix of p-value
sig.lvl	a numeric vector specifying significant level(s). If the p-value in p.mat is bigger than sig.lvl (0.05 by default), then the corresponding correlation coefficient is regarded as insignificant. If insig is "label_sig", this may be an increasing vector of significance levels, for example c(0.05, 0.01, 0.001), in which case pch will be used once for the highest p-value interval and multiple times (e.g. "*", "**", "***") for each lower p-value interval.
number.digits	the number of decimal digits (2 by default) while the visualization method is "number".
insig	a character specialized insignificant correlation coefficients, "pch" (default), "blank", or "label_sig".
pch	a point character indicating the shape of insignificant correlation coefficients.
pch.cex	a number controlling the shape size of insignificant correlation coefficients.

caijun/ggcorrplot2 documentation built on May 15, 2022, 1:28 a.m.