R: Correlation Plot

corrplot {arm} | R Documentation

## Correlation Plot

### Description

Function for making a correlation plot starting from a data matrix

### Usage

    corrplot (data, varnames=NULL, cutpts=NULL,
        abs=TRUE, details=TRUE,
        n.col.legend=5, cex.col=0.7,
        cex.var=0.9, digits=1, color=FALSE)

### Arguments

`data` | a data matrix
`varnames` | variable names of the data matrix, if not provided use default variable names
`abs` | if TRUE, transform all correlation values into positive values, default=TRUE.
`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 adapts the R function for Figure 8 in Tian Zheng, Matthew Salganik, and Andrew Gelman, 2006, "How many people do you know in prison?: using overdispersion in count data to estimate social structure in networks", Journal of the American Statistical Association, Vol.101, N0. 474: p.409-23.

### Value

A correlation plot.

### Author(s)

Tian Zheng tzheng@stat.columbia.edu; Yu-Sung Su suyusung@tsinghua.edu.cn

### References

Tian Zheng, Matthew Salganik, and Andrew Gelman, 2006, "How many people do you know in prison?: using overdispersion in count data to estimate social structure in networks", Journal of the American Statistical Association, Vol.101, N0. 474: p.409-23

### See Also

`cor`, `par`

### Examples

    old.par <- par(no.readonly = TRUE)

     x1 <- rnorm(1000,50,2)
     x2 <- rbinom(1000,1,prob=0.63)
     x3 <- rpois(1000, 2)
     x4 <- runif(1000,40,100)
     x5 <- rnorm(1000,100,30)
     x6 <- rbeta(1000,2,2)
     x7 <- rpois(1000,10)
     x8 <- rbinom(1000,1,prob=0.4)
     x9 <- rbeta(1000,5,4)
     x10 <- runif(1000,-10,-1)

     test.data <- data.matrix(cbind(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10))
     test.names <- c("a short name01","a short name02","a short name03",
                     "a short name04","a short name05","a short name06",
                     "a short name07","a short name08","a short name09",
                     "a short name10")

     # example 1
     corrplot(test.data)

     # example 2
     corrplot(test.data,test.names, abs=FALSE, n.col.legend=7)
     corrplot(test.data,test.names, abs=TRUE, n.col.legend=7)

     # example 3
     data(lalonde)
     corrplot(lalonde, details=FALSE, color=TRUE)
     corrplot(lalonde, cutpts=c(0,0.25,0.5,0.75), color=TRUE, digits=2)

    par(old.par)

[Package arm version 1.9-3 Index]