bs2pbs {CorrToolBox} | R Documentation |
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).
bs2pbs(bs, bin.var, cont.var, p=NULL, cutpoint=NULL)
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 |
cutpoint |
The value at which the numeric vector |
The point-biserial correlation.
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.
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)