Power Calculations for Longitudinal Multilevel Models

Calculate power for 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) , to three-level models. Additionally, the simulation tools provides flexible ways to investigate bias, type I errors and the consequences of model misspecification.


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install.packages("powerlmm")

0.1.0 by Kristoffer Magnusson, 8 days ago


https://github.com/rpsychologist/powerlmm


Report a bug at https://github.com/rpsychologist/powerlmm/issues


Browse source code at https://github.com/cran/powerlmm


Authors: Kristoffer Magnusson [aut, cre]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports stats, lmerTest, lme4, ggplot2, ggsci, pbmcapply, Matrix, MASS, gridExtra, scales, utils, testthat

Suggests dplyr, tidyr, knitr, rmarkdown, viridis, shiny, shinydashboard


See at CRAN