lme4
7 posts
Do you really need a multilevel model? A preview of powerlmm 0.4.0
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.
Power analysis for longitudinal multilevel models: powerlmm 0.3.0 is now out on CRAN
My R package 'powerlmm' has now been update to version 0.3.0. It adds support for a more flexible effect size specifiation.
Slides from my talk on how to do power analysis for longitudinal 2- and 3-level models.
This post contains the slides from a talk I gave recently at Stockholm University
Power analysis for longitudinal multilevel models: powerlmm 0.2.0 is now out on CRAN
My R package 'powerlmm' has now been update to version 0.2.0. It contains several improvements, and new features.
Are parallel simulations in the cloud worth it? Benchmarking my MBP vs my Workstation vs Amazon EC2
In this post I compare the performance of Amazon EC2 instances vs my HP workstation and my MacBook Pro, when doing Monte Carlo simulations.
Introducing 'powerlmm' an R package for power calculations for longitudinal multilevel models
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.
Using R and lme/lmer to fit different two- and three-level longitudinal models
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.