In this post I show how to make marginal inferences on the untransformed scaled when using multilevel models with a non-linear transformation applied to the dependent variable (a log-transformation is used as an example). Cluster-specific versus population-average (conditional versus marginal) effects are compared using both average effects on the untransformed scale and using relative (multiplicative) effects.

The next version of powerlmm (0.4.0) will soon be released, besides bug fixes this version also includes several new simulation features. In this post I will show two examples that cover the major new features.

My R package 'powerlmm' has now been update to version 0.3.0. It adds support for a more flexible effect size specifiation.

This post contains the slides from a talk I gave recently at Stockholm University

My R package 'powerlmm' has now been update to version 0.2.0. It contains several improvements, and new features.

Over the summer I've been working on finishing my new R package 'powerlmm', which is now almost complete. It provides flexible power calculations for typical two- and three-level longitudinal linear mixed models, with unbalanced treatment groups and cluster sizes, as well as with missing data and random slopes at both the subject and cluster-level.

I often get asked about how to fit different longitudinal models in lme/lmer. In this post I cover several different two-level, three-level and partially nested models.