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 ys463@columbia.edu

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 1.3-08 Index]