powerlmm: Power Analysis for Longitudinal Multilevel Models

Description Details Supported models Tutorials Author(s) See Also

Description

The powerlmm package provides a fast and flexible way to calculate power for two- and three-level multilevel models with missing data. The focus is on power analysis for the test of the treatment effect in longitudinally clustered designs, i.e. where the first level is measurements, and the second level is subjects nested within a (optional) higher level-three unit, e.g. therapists.

Details

All study designs are specified using the function study_parameters, which lets you define your model using familiar notation, either by specifying the model parameters directly, or by using relative standardized inputs (e.g. % variance at each level). Several functions are provided to help you visualize and understand the implied model, type methods(class="plcp") to see available methods. The basic features of the package are also available via an interactive (Shiny) web application, which you can launch by typing shiny_powerlmm().

Supported models

The purpose of powerlmm is to help design longitudinal treatment studies, with or without higher-level clustering (e.g. by therapists, groups, or physicians), and missing data. The main features of the package are:

Tutorials

Type vignette("two-level", package = "powerlmm"), or vignette("three-level", package = "powerlmm") to see a tutorial on using powerlmm to calculate power. See all available vignettes by typing vignette(package = "powerlmm").

Author(s)

Kristoffer Magnusson

Maintainer: Kristoffer Magnusson <[email protected]>

See Also

study_parameters, get_power


rpsychologist/powerlmm documentation built on April 19, 2018, 7:21 p.m.