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multilevelTools

Effect sizes, diagnostics and performance metrics for multilevel and mixed effects models. Includes marginal and conditional R2 estimates for linear mixed effects models based on Johnson 2014.

The vignette shows a complete example.

Installation

To get the version from CRAN:


install.packages("multilevelTools")

To get the latest development version, use:


#install.packages("devtools")
devtools::install_github("JWiley/multilevelTools")

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Version

Install

install.packages('multilevelTools')

Monthly Downloads

523

Version

0.1.1

License

GPL (>= 3)

Issues

2

Pull Requests

0

Stars

4

Forks

2

Maintainer

Joshua F Wiley

Last Published

March 4th, 2020

Functions in multilevelTools (0.1.1)

APAStyler.modelTest.merMod

Format results from a linear mixed model
R2.merMod

merMod method for R2
modelDiagnostics.merMod

modelDiagnostics method for merMod objects
modelDiagnostics.lme

modelDiagnostics method for lme objects
modelCompare.merMod

Compare two lmer models
modelPerformance.merMod

modelPerformance method for merMod objects
modelTest.merMod

estimate detailed results per variable and effect sizes for both fixed and random effects from lmer models
nEffective

Estimate the effective sample size from longitudinal data
residualDiagnostics.merMod

residualDiagnostics methods for merMod objects
plot.modelDiagnostics.merMod

Plot Diagnostics for an lmer model
residualDiagnostics.lme

residualDiagnostics methods for lme objects
iccMixed

Intraclass Correlation Coefficient (ICC) from Mixed Models
acfByID

Estimate the autocorrelation by unit (ID)
omegaSEM

Calculate multilevel omega reliability
plot.modelDiagnostics.lme

Plot Diagnostics for an lme model
meanDecompose

Mean decomposition of a variable by group(s)
meanDeviations

Function to calculate the mean and deviations from mean