Power analysis
9 posts
Introducing PowerLMM.js: Browser-based power analysis for longitudinal models with dropout
A new tool for power analysis of longitudinal linear mixed-effects models (LMMs), with support for missing data, random slopes, and non-inferiority and equivalence tests.
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.
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.
New d3.js visualization: Understanding Significance Testing and Statistical Power
This new visualization is an interactive display of classical null hypothesis significance testing and statistical power.
Creating a typical textbook illustration of statistical power using either ggplot or base graphics
A common way of illustrating the idea behind statistical power in null hypothesis significance testing, is by plotting the sampling distributions of the null hypothesis and the alternative hypothesis. Typically, these illustrations highlight the regions that correspond to making a type II error, type I error and correctly rejecting the null hypothesis (i.e. the test’s power). In this post I will show how to create such “power plots” using both ggplot and R’s base graphics.
Analytical and simulation-based power analyses for mixed-design ANOVAs
In this post I show some R-examples on how to perform power analyses for mixed-design ANOVAs. The first example is analytical—and adapted from formulas used in G*Power (Faul et al., 2007), and the second example is a Monte Carlo simulation.