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-release: powerlmm_0.4.0.zip, r-oldrel: powerlmm_0.4.0.zip |
| macOS binaries: | r-release: powerlmm_0.4.0.tgz, r-oldrel: powerlmm_0.4.0.tgz |
| Old sources: | powerlmm archive |
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