I have two logistic mixed-effects models, nested within each other and differing in only one fixed variable:
mod1<- glmer(y ~ x1 + x2 + (x1 + x2| subject_ID), data = dat, family = binomial)
mod2<- glmer(y ~ x1 + (x1 + x2| subject_ID), data = dat, family = binomial)
I use the R
function anova
to compare the two with test="Chisq"
, which gives me a likelihood ratio test of whether the fixed effect missing from mod2 significantly improves mod1. I would like to calculate the effect size (specifically, Cohen's w) for this test.
How can I accomplish this?
Relatedly, it seems that to calculate Cramer's V or Cohen's w I need to know the sample size, number of rows, and number of columns of my test. What do rows and columns refer to in this case?