P-values from random effects linear regression models

lme4::lmer is a useful frequentist approach to hierarchical/multilevel linear regression modelling. For good reason, the model output only includes t-values and doesn’t include p-values (partly due to the difficulty in estimating the degrees of freedom, as discussed here).

Yes, p-values are evil and we should continue to try and expunge them from our analyses. But I keep getting asked about this. So here is a simple bootstrap method to generate two-sided parametric p-values on the fixed effects coefficients. Interpret with caution.

 

4 Comments

on “P-values from random effects linear regression models
4 Comments on “P-values from random effects linear regression models
  1. Pingback: P-values from random effects linear regression models – Cloud Data Architect

  2. Pingback: P-values from random effects linear regression models – Mubashir Qasim

    • Thanks For your interest Jason. Yes you can use lmerTest. Douglas Bates didn’t include what he refers to as “SAS” p-values in the lmer output, as derived using lmerTest, hence the presented bootstrap method.

      I’ve moved over to Bayesian methods and will post on mixed models using Stan soon. Thanks again.

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