R: Create a linear mixed-effect model loop that removes one fixed independent variable, then adds it back in and removes another

Question

I'm looking for a way to create a linear mixed-effect model loop that removes one fixed effect independent variable, runs the model, adds it back in, and removes another fixed independent variable from the model.

For example:

library(lme4)

L1 <- lmer(cyl ~ disp + hp + mpg (carb|gear), mtcars)

How can I create a loop of this model so that each new regression loop iteration removes a single fixed effect independent variable, adds it back in on the next iteration, and removes a different fixed independent variable?

Doing this would produce these models:

Original full model: lmer(cyl ~ disp + mpg + (carb|gear), mtcars)

Partial model with mpg removed: lmer(cyl ~ disp + hp + (carb|gear), mtcars)

Partial model with mpg re-added and disp removed: lmer(cyl ~ hp + mpg (carb|gear), mtcars)

Partial model with disp re-added and hp removed: lmer(cyl ~ disp + mpg (carb|gear), mtcars)

I am also curious how I can add each model to a list after each loop iteration.

Thanks a lot!

Answer 1

Check out the MuMIn package for model averaging. You normally start by running the full model, and the dredge function can be used to iteratively run models with all combinations of predictors. You can specify effects that you want to keep consistent e.g. your (carb|gear) random effect. The outputs can give you a table of results for each model.