Package: mlmpower 1.0.11

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]

mlmpower_1.0.11.tar.gz
mlmpower_1.0.11.zip(r-4.7-any)mlmpower_1.0.11.zip(r-4.6-any)mlmpower_1.0.11.zip(r-4.5-any)
mlmpower_1.0.11.tgz(r-4.6-any)mlmpower_1.0.11.tgz(r-4.5-any)
mlmpower_1.0.11.tar.gz(r-4.7-any)mlmpower_1.0.11.tar.gz(r-4.6-any)
mlmpower_1.0.11.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mlmpower/json (API)

# 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:

4.30 score 4 stars 4 scripts 364 downloads 22 exports 88 dependencies

Last updated from:ab9e570451. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	152
source / vignettes	OK	194
linux-release	OK	153
macos-release	OK	186
macos-oldrel	OK	168
windows-devel	OK	72
windows-release	OK	116
windows-oldrel	OK	112
wasm-release	OK	122

Exports:analyze between_binary_predictor between_predictor center correlations cross_sectional effect_size fixed generate is_valid longitudinal MAR MCAR outcome parameters power_analysis product random random_slope to_formula within_predictor within_time_predictor

Dependencies:alabama anocva boot cli cluster codetools CompQuadForm corpcor cpp11 diagonals distributional doParallel dplyr expm farver forcats foreach generics ggdist ggplot2 ggrepel glue gridExtra gtable HLMdiag hms isoband iterators janitor labeling lattice lavaan lifecycle lme4 lmeresampler lmerTest lubridate magrittr MASS Matrix mclust merDeriv mgcv minqa mnormt msm mvtnorm nlme nlmeU nloptr nonnest2 npde numDeriv pbivnorm pillar pkgconfig plyr purrr quadprog R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas reshape2 rlang S7 saemix sandwich scales snakecase statmod stringi stringr survival tibble tidyr tidyselect timechange utf8 varTestnlme vctrs viridisLite withr zoo

Using mlmpower Package to Conduct Multilevel Power Analysis

Illustration 1: Cross-Sectional Power Analysis | Illustration 2: Growth Curve Power Analysis | Illustration 3 Vignette: Cluster-Randomized Design

Rendered frommlmpower.Rmdusingknitr::rmarkdown

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

Help page	Topics
Analyzes a single 'mp_data' using 'lme4::lmer'	analyze
Coerce a 'mp_power' to a Data Frame	as.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 Model	correlations mp_corr mp_correlations
Specify the Effect Size for the Model	cross_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 Specified	is_valid
Obtain Level of Observation for a Variable	levels.mp_variable
Helper functions for producing Missing Data Mechanisms	MAR MCAR mechanism mechanisms
'mlmpower' Modeling Framework	mlmpower model Modeling modeling mp_action mp_base mp_model `+.mp_base`
Functions for Generating Correlations	fixed mp_corr_func random
'mp_parameters' Object for 'mlmpower'	mp_parameters
Obtain 'mp_parameters' from objects	parameters 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 Model	product
Create a Random Slope in a Model	random_slope
Subset a 'mp_model' by Global ICC	subset.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 Variables	between_binary_predictor between_predictor mp_variable outcome variable Variables variables within_predictor within_time_predictor

Package: mlmpower 1.0.11

mlmpower: Power Analysis and Data Simulation for Multilevel Models

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Usage by other packages (reverse dependencies)