biserial.cor: Point-Biserial Correlation

In drizopoulos/ltm: Latent Trait Models under IRT

Point-Biserial Correlation

Description

Computes the point-biserial correlation between a dichotomous and a continuous variable.

Usage

biserial.cor(x, y, use = c("all.obs", "complete.obs"), level = 1)

Arguments

x	a numeric vector representing the continuous variable.
y	a factor or a numeric vector (that will be converted to a factor) representing the dichotomous variable.
use	If use is "all.obs", then the presence of missing observations will produce an error. If use is "complete.obs" then missing values are handled by casewise deletion.
level	which level of y to use.

Details

The point biserial correlation computed by biserial.cor() is defined as follows

(X1.bar - X0.bar) * sqrt(pi * (1 - pi)) / S_x,

where X1.bar and X0.bar denote the sample means of the X-values corresponding to the first and second level of Y, respectively, S_x is the sample standard deviation of X, and pi is the sample proportion for Y = 1. The first level of Y is defined by the level argument; see Examples.

Value

the (numeric) value of the point-biserial correlation.

Note

Changing the order of the levels for y will produce a different result. By default, the first level is used as a reference level

Author(s)

Dimitris Rizopoulos [e-mail]

Examples

# the point-biserial correlation between
# the total score and the first item, using
# '0' as the reference level
biserial.cor(rowSums(LSAT), LSAT[[1]])

# and using '1' as the reference level
biserial.cor(rowSums(LSAT), LSAT[[1]], level = 2)

drizopoulos/ltm documentation built on March 25, 2022, 4:46 a.m.