bs2pbs {CorrToolBox} R Documentation

## Computation of the Point-Biserial Correlation from the Biserial Correlation

### Description

This function computes the point-biserial correlation between two variables after one of the variables is dichotomized given the correlation before dichotomization (biserial correlation) as seen in Demirtas and Hedeker (2016). Before computation of the point-biserial correlation, the specified biserial correlation is compared to the lower and upper correlation bounds of the two continuous variables using the generate, sort and correlate (GSC) algorithm in Demirtas and Hedeker (2011).

### Usage

bs2pbs(bs, bin.var, cont.var, p=NULL, cutpoint=NULL)


### Arguments

 bs The biserial correlation. bin.var A numeric vector of the continuous variable before dichotomization. cont.var A numeric vector of the continuous variable that is not transformed. p The expected value of the numeric vector bin.var after dichotomization. Either p or cutpoint should be specified. cutpoint The value at which the numeric vector bin.var should be dichotomized. Either p or cutpoint should be specified.

### Value

The point-biserial correlation.

### References

Demirtas, H. and Hedeker, D. (2011). A practical way for computing approximate lower and upper correlation bounds. The American Statistician, 65(2), 104-109.

Demirtas, H. and Hedeker, D. (2016). Computing the point-biserial correlation under any underlying continuous distribution. Communications in Statistics-Simulation and Computation, 45(8), 2744-2751.

### Examples

set.seed(123)
y1<-rweibull(n=100000, scale=1, shape=1.2)

gaussmix <- function(n,m1,m2,s1,s2,pi) {
I <- runif(n)<pi
rnorm(n,mean=ifelse(I,m1,m2),sd=ifelse(I,s1,s2))
}
y2<-gaussmix(n=100000, m1=0, s1=1, m2=3, s2=1, pi=0.6)

bs2pbs(bs=0.6, bin.var=y1, cont.var=y2, p=0.55)
bs2pbs(bs=0.6, bin.var=y1, cont.var=y2, cutpoint=0.65484)


[Package CorrToolBox version 1.6.4 Index]