Solved – standardized coefficients from glm logit

I am trying to create a coefficient plot from multiple logistic regression models, which all have the same predictors, but different sample sizes. This is a pre-test to a multilevel model. My question is two fold:

  1. Given, I want to compare the effect sizes of the same predictor in the different models, I assume I need to use standardized coefficients. How does one calculate standardized coefficients in a logit model?

  2. Is there an easy way to estimate such coefficients in R? For instance with OLS, I could rely on the "lm.beta" function from the QuantPsyc package. I am wondering, is there a functional equivalent for a glm logit? I could not find an immediate solution myself.

If the predictors you used in the different regression models are measured in the same way it is no longer necessary to normalize the coefficients, since the predictors are already on the same scale the coefficients are as well.

If not, first try to rescale the predictor variables.

Similar Posts:

Rate this post

Leave a Comment