chisq2S: Compute the robust effect size index estimate from...

In RESI: Robust Effect Size Index (RESI) Estimation

Compute the robust effect size index estimate from chi-squared statistic.

Description

This function computes the robust effect size index from Vandekar, Tao, & Blume (2020). Vector arguments are accepted. If different length arguments are passed they are dealt with in the usual way of R. For mixed effects models, RESI is conditional on the average correlation structure within subjects.

Usage

chisq2S(chisq, df, n)

Arguments

chisq	The chi-square statistic for the parameter of interest.
df	Number of degrees of freedom of the chi-square statistic.
n	Number of independent samples.

Details

The formula for converting a Chi-square statistic to RESI is:

S = \sqrt(max( 0, (chisq - df)/n))

Value

Returns a scalar or vector argument of the robust effect size index estimate.

Examples

# obtain Chi-sq value by fitting an lm and running a Wald test
mod = lm(charges ~ region * age + bmi + sex, data = RESI::insurance)

# run a Wald test with robust variance
wt = lmtest::waldtest(mod, vcov = sandwich::vcovHC, test = "Chisq")

# get Chi-sq value and degrees of freedom
chisq = wt$Chisq[2]
df = abs(wt$Df[2])

# run chisq2S to convert to RESI
chisq2S(chisq, df = df, n = nrow(mod$model))

RESI documentation built on April 3, 2025, 5:26 p.m.