Package: mlmpower 1.0.9

mlmpower: Power Analysis and Data Simulation for Multilevel Models

A declarative language for specifying multilevel models, solving for population parameters based on specified variance-explained effect size measures, generating data, and conducting power analyses to determine sample size recommendations. The specification allows for any number of within-cluster effects, between-cluster effects, covariate effects at either level, and random coefficients. Moreover, the models do not assume orthogonal effects, and predictors can correlate at either level and accommodate models with multiple interaction effects.

Authors:Brian T. Keller [aut, cre, cph]

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mlmpower.pdf |mlmpower.html✨
mlmpower/json (API)
NEWS

# Install 'mlmpower' in R:
install.packages('mlmpower', repos = c('https://bkeller2.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bkeller2/mlmpower/issues

On CRAN:

Conda:

4.65 score 3 stars 3 scripts 330 downloads 22 exports 90 dependencies

Last updated 5 months agofrom:101735ea0a. Checks:6 OK, 3 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 19 2025
R-4.5-winNOTEMar 19 2025
R-4.5-macNOTEMar 19 2025
R-4.5-linuxNOTEMar 19 2025
R-4.4-winOKMar 19 2025
R-4.4-macOKMar 19 2025
R-4.4-linuxOKMar 19 2025
R-4.3-winOKMar 19 2025
R-4.3-macOKMar 19 2025

Exports:analyzebetween_binary_predictorbetween_predictorcentercorrelationscross_sectionaleffect_sizefixedgenerateis_validlongitudinalMARMCARoutcomeparameterspower_analysisproductrandomrandom_slopeto_formulawithin_predictorwithin_time_predictor

Dependencies:alabamaanocvabootcliclustercodetoolscolorspaceCompQuadFormcorpcorcpp11diagonalsdistributionaldoParalleldplyrexpmfansifarverforcatsforeachgenericsggdistggplot2ggrepelgluegridExtragtableHLMdiaghmsisobanditeratorsjanitorlabelinglatticelavaanlifecyclelme4lmeresamplerlmerTestlubridatemagrittrMASSMatrixmclustmerDerivmgcvminqamnormtmsmmunsellmvtnormnlmenlmeUnloptrnonnest2npdenumDerivpbivnormpillarpkgconfigplyrpurrrquadprogR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rlangsaemixsandwichscalessnakecasestatmodstringistringrsurvivaltibbletidyrtidyselecttimechangeutf8varTestnlmevctrsviridisLitewithrzoo

Using mlmpower Package to Conduct Multilevel Power Analysis

Brian T. Keller

Rendered frommlmpower.Rmdusingknitr::rmarkdownon Mar 19 2025.

Last update: 2024-04-03
Started: 2023-04-22

Readme and manuals

Help Manual

Help pageTopics
Analyzes a single 'mp_data' using 'lme4::lmer'analyze
Coerce a 'mp_power' to a Data Frameas.data.frame.mp_power
Convert 'mp_parameters' to a 'list'as.list.mp_parameters
Center a data set based on a 'mp_data'center
Specify the Correlation Structure for the Modelcorrelations mp_corr mp_correlations
Specify the Effect Size for the Modelcross_sectional effect_size longitudinal mp_effect mp_effsize
Generates Data Sets Based on a 'mp_model'generate mp_data
Check if a Model is Properly Specifiedis_valid
Obtain Level of Observation for a Variablelevels.mp_variable
Helper functions for producing Missing Data MechanismsMAR MCAR mechanism mechanisms
'mlmpower' Modeling Frameworkmlmpower model Modeling modeling mp_action mp_base mp_model `+.mp_base`
Functions for Generating Correlationsfixed mp_corr_func random
'mp_parameters' Object for 'mlmpower'mp_parameters
Obtain 'mp_parameters' from objectsparameters parameters.mp_data parameters.mp_model parameters.mp_power
Conduct a Power Analysis Based on 'mp_model'mp_power power_analysis
Prints a 'mp_correlations'print.mp_correlations
Prints a 'mp_effsize'print.mp_effsize
Prints a 'mp_model'print.mp_model
Prints a 'mp_parameters'print.mp_parameters
Prints a 'mp_power'print.mp_power
Prints a 'mp_variable'print.mp_variable
Create a Product Term in a Modelproduct
Create a Random Slope in a Modelrandom_slope
Subset a 'mp_model' by Global ICCsubset.mp_model
Obtain the Parameter Summaries for A 'mp_model'summary.mp_model
Summarizes a 'mp_power'summary.mp_power
Convert 'mp_data' to a 'stats::formula' to be used for 'lme4::lmer'to_formula
Functions for Creating Variablesbetween_binary_predictor between_predictor mp_variable outcome variable Variables variables within_predictor within_time_predictor