# triangleplot: Triangle Plot In arm: Data Analysis Using Regression and Multilevel/Hierarchical Models

## Description

Function for making a triangle plot from a square matrix

## Usage

 ```1 2 3``` ```triangleplot (x, y=NULL, cutpts=NULL, details=TRUE, n.col.legend=5, cex.col=0.7, cex.var=0.9, digits=1, color=FALSE) ```

## Arguments

 `x` a square matrix. `y` a vector of names that corresponds to each element of the square matrix x. `cutpts` a vector of cutting points for color legend, default is `NULL`. The function will decide the cutting points if cutpts is not assigned. `details` show more than one digits correlaton values. Default is `TRUE`. `FALSE` is suggested to get readable output. `n.col.legend` number of legend for the color thermometer `cex.col` font size of the color thermometer. `cex.var` font size of the variable names. `digits` number of digits shown in the text of the color theromoeter. `color` color of the plot, default is FALSE, which uses gray scale.

## Details

The function makes a triangle plot from a square matrix, e.g., the correlation plot, see `corrplot`. If a square matrix contains missing values, the cells of missing values will be marked `x`.

## Author(s)

Yu-Sung Su [email protected]

`corrplot`, `par`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```old.par <- par(no.readonly = TRUE) # create a square matrix x <- matrix(runif(1600, 0, 1), 40, 40) # fig 1 triangleplot(x) # fig 2 assign cutting points triangleplot(x, cutpts=c(0,0.25,0.5,0.75,1), digits=2) # fig 3 if x contains missing value x[12,13] <- x[13,12] <- NA x[25,27] <- x[27,25] <- NA triangleplot(x) par(old.par) # #library(RColorBrewer) #cormat <- cor(iris[,-5]) #triangleplot2(cormat,color = brewer.pal( 5, "RdBu" ), # n.col.legend=5, cex.col=0.7, cex.var=0.5) ```