Computes polychoric and polyserial correlations by quick "two-step" methods or ML, optionally with standard errors; tetrachoric and biserial correlations are special cases.
The polycor package computes polychoric and polyserial correlations by quick “two-step” methods or ML, optionally with standard errors; tetrachoric and biserial correlations are special cases.
It was originally written by Prof. John Fox, who passed away in November, 2025. I (Duncan Murdoch) have taken over as maintainer in order to keep the package available as R evolves.
Please submit bug reports as Github issues at https://github.com/dmurdoch/polycor/issues.
You can install polycor from CRAN using:
install.packages("polycor")
You can install the development version of polycor from GitHub with:
# install.packages("pak")
pak::pak("dmurdoch/polycor")
library(mvtnorm)
library(polycor)
set.seed(12345)
data <- rmvnorm(1000, c(0, 0), matrix(c(1, .5, .5, 1), 2, 2))
x <- data[,1]
y <- data[,2]
cor(x, y) # sample correlation
#> [1] 0.5263698
x <- cut(x, c(-Inf, .75, Inf))
y <- cut(y, c(-Inf, -1, .5, 1.5, Inf))
polychor(x, y) # 2-step estimate
#> [1] 0.5230474
polychor(x, y, ML=TRUE, std.err=TRUE) # ML estimate
#>
#> Polychoric Correlation, ML est. = 0.5231 (0.03819)
#> Test of bivariate normality: Chisquare = 2.739, df = 2, p = 0.2543
#>
#> Row Threshold
#> Threshold Std.Err.
#> 0.7537 0.04403
#>
#>
#> Column Thresholds
#> Threshold Std.Err.
#> 1 -0.9842 0.04746
#> 2 0.4841 0.04127
#> 3 1.5010 0.06118