powerlmm: Power Analysis for Longitudinal Multilevel Models

Calculate power for the 'time x treatment' effect in two- and three-level multilevel longitudinal studies with missing data. Both the third-level factor (e.g. therapists, schools, or physicians), and the second-level factor (e.g. subjects), can be assigned random slopes. Studies with partially nested designs, unequal cluster sizes, unequal allocation to treatment arms, and different dropout patterns per treatment are supported. For all designs power can be calculated both analytically and via simulations. The analytical calculations extends the method described in Galbraith et al. (2002) <doi:10.1016/S0197-2456(02)00205-2>, to three-level models. Additionally, the simulation tools provides flexible ways to investigate bias, Type I errors and the consequences of model misspecification.

Version: 0.4.0
Depends: R (≥ 3.2.0)
Imports: stats, methods, parallel, lme4 (≥ 1.1), Matrix, MASS, scales, utils
Suggests: testthat, dplyr, tidyr, knitr, rmarkdown, pbmcapply (≥ 1.1), lmerTest (≥ 2.0), ggplot2 (≥ 2.2), ggsci, viridis, gridExtra, shiny (≥ 1.0), shinydashboard
Published: 2018-08-14
Author: Kristoffer Magnusson [aut, cre]
Maintainer: Kristoffer Magnusson <hello at kristoffer.email>
BugReports: https://github.com/rpsychologist/powerlmm/issues
License: GPL (≥ 3)
URL: https://github.com/rpsychologist/powerlmm
NeedsCompilation: no
Materials: README NEWS
In views: MissingData
CRAN checks: powerlmm results


Reference manual: powerlmm.pdf
Vignettes: Tutorial: Evaluate the Models Using Monte Carlo Simulations
Details on the Power Calculations
Tutorial: Three-level Longitudinal Power Analysis
Tutorial: Two-level Longitudinal Power Analysis
Package source: powerlmm_0.4.0.tar.gz
Windows binaries: r-devel: powerlmm_0.4.0.zip, r-devel-gcc8: powerlmm_0.4.0.zip, r-release: powerlmm_0.4.0.zip, r-oldrel: powerlmm_0.4.0.zip
OS X binaries: r-release: powerlmm_0.4.0.tgz, r-oldrel: powerlmm_0.4.0.tgz
Old sources: powerlmm archive


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