Introduction | Prerequisite | Scope | Package | Workflow | Mediation | Specify the Population Model | Specify The Population Values | Specify the Measurement Part | Call power4test() to Check the Model | Call power4test() to Do the Target Test | Compute the Power | Repeat a Simulation With A Different Sample Size | Repeat a Simulation With Different Numbers of Indicators or Reliability | Find the Sample Size With Desired Power | Using n_region_from_power() | Using power4test_by_n() | Using x_from_power() | Other Scenarios | References
Package: power4mome 0.2.1.5
power4mome: Power Analysis for Moderation and Mediation
Power analysis and sample size determination for moderation, mediation, and moderated mediation in models fitted by structural equation modelling using the 'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02> or by multiple regression. The package 'manymome' by Cheung and Cheung (2024) <doi:10.3758/s13428-023-02224-z> is used to specify the indirect paths or conditional indirect paths to be tested.
Authors:
power4mome_0.2.1.5.tar.gz
power4mome_0.2.1.5.zip(r-4.7)power4mome_0.2.1.5.zip(r-4.6)power4mome_0.2.1.5.zip(r-4.5)
power4mome_0.2.1.5.tgz(r-4.6-any)power4mome_0.2.1.5.tgz(r-4.5-any)
power4mome_0.2.1.5.tar.gz(r-4.7-any)power4mome_0.2.1.5.tar.gz(r-4.6-any)
power4mome_0.2.1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
power4mome/json (API)
| # Install 'power4mome' in R: |
| install.packages('power4mome', repos = c('https://sfcheung.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/sfcheung/power4mome/issues
Pkgdown/docs site:https://sfcheung.github.io
lavaanmediationmoderated-mediationmoderationpower-analysissample-sizesemstructural-equation-modeling
Last updated from:b2cf8ca49d. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 363 | ||
| source / vignettes | OK | 289 | ||
| linux-release-x86_64 | OK | 341 | ||
| macos-release-arm64 | OK | 259 | ||
| macos-oldrel-arm64 | OK | 276 | ||
| windows-devel | OK | 325 | ||
| windows-release | OK | 348 | ||
| windows-oldrel | OK | 312 | ||
| wasm-release | OK | 159 |
Exports:arg_x_from_poweras.power4test_by_esas.power4test_by_ncut_patternsdo_testfind_par_namesfit_modelgen_bootgen_mcmissing_valuesmodel_matrices_popn_from_powern_region_from_powerordinal_variablespba_diagnosispool_sim_datapop_es_yamlpower_curvepower4testpower4test_by_espower4test_by_nptable_popq_power_mediationq_power_mediation_parallelq_power_mediation_serialq_power_mediation_simpleR_for_bzrbeta_rsrbeta_rs2rbinary_rsrejection_ratesrexp_rsrlnorm_rsrpgnorm_rsRs_bz_supportedrt_rsrunif_rsscale_scoressim_datasim_outsummarize_teststest_cond_indirecttest_cond_indirect_effectstest_group_equaltest_index_of_mometest_indirect_effecttest_k_indirect_effectstest_moderationtest_parametersx_from_power
Dependencies:backportsbitbit64bootbroomclicliprcodetoolscpp11crayondplyrfarverforcatsforeachgenericsggplot2glmnetglueGPArotationgtablehavenhmsigraphisobanditeratorsjomolabelinglatticelavaanlifecyclelme4lmhelprsmagrittrmanymomeMASSMatrixmiceminqamitmlmnormtnlmenloptrnnetnumDerivordinalpanpbapplypbivnormpgnormpillarpkgconfigprettyunitsprogresspsychpurrrquadprogR6rbibutilsRColorBrewerRcppRcppEigenRdpackreadrreformulasrlangrpartS7scalessemToolsshapestringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vctrsviridisLitevroomwithryaml
Power Analysis for Latent Variable Mediation
Last update: 2026-03-10
Started: 2025-05-30
Power Analysis for Moderation, Mediation, and Moderated Mediation
Introduction | Prerequisite | Scope | Package | Workflow | Mediation | Specify the Population Model | Specify The Population Values | Call power4test() to Check the Model | Call power4test() to Do the Target Test | Compute the Power | Moderation | Specify the Population Model and Values | Call power4test() to Test The Moderation Effect | Moderated mediation | Specify the Population Model and Values | Call power4test() to Test The Moderated Mediation Effect | Repeating a Simulation With A Different Sample Size | Find the Sample Size With The Desired Power | Using n_region_from_power() | Using power4test_by_n() | Using x_from_power() | Other Advanced Features | Limitations | References
Last update: 2026-03-10
Started: 2025-02-22
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Helpers for the Boos-and-Zhang (2000) Method | bz_helpers Rs_bz_supported R_for_bz |
| Do a Test on Each Replication | do_test |
| Fit a Model to a List of Datasets | fit_model |
| Generate Bootstrap Estimates | gen_boot |
| Generate Monte Carlo Estimates | gen_mc |
| Process Data by Generating Missing Values | missing_values |
| Process Data by Creating Ordinal Variables | cut_patterns ordinal_variables |
| Plot a Power Curve | plot.power4test_by_es plot.power4test_by_n plot.power_curve |
| Plot The Results of 'x_from_power' | plot.n_region_from_power plot.x_from_power |
| Parse YAML-Stye Values For 'pop_es' | pop_es_yaml |
| Power Curve | power_curve print.power_curve |
| Estimate the Power of a Test | power4test print.power4test |
| Power By Effect Sizes | as.power4test_by_es c.power4test_by_es power4test_by_es print.power4test_by_es |
| Power By Sample Sizes | as.power4test_by_n c.power4test_by_n power4test_by_n print.power4test_by_n |
| Predict Method for a 'power_curve' Object | predict.power_curve |
| Generate the Population Model | model_matrices_pop ptable_pop |
| All-in-One Power Estimation For Mediation Models | plot.q_power_mediation print.q_power_mediation q_power_mediation q_power_mediation_parallel q_power_mediation_serial q_power_mediation_simple summary.q_power_mediation |
| Random Variable From a Beta Distribution | rbeta_rs |
| Random Variable From a Beta Distribution (User Range) | rbeta_rs2 |
| Random Binary Variable | rbinary_rs |
| Rejection Rates | print.rejection_rates_df rejection_rates rejection_rates.default rejection_rates.n_region_from_power rejection_rates.power4test rejection_rates.power4test_by_es rejection_rates.power4test_by_n rejection_rates.q_power_mediation rejection_rates.x_from_power |
| Random Variable From an Exponential Distribution | rexp_rs |
| Random Variable From a Lognormal Distribution | rlnorm_rs |
| Random Variable From a Generalized Normal Distribution | rpgnorm_rs |
| Random Variable From a t Distribution | rt_rs |
| Random Variable From a Uniform Distribution | runif_rs |
| Process Data by Computing Scale Scores | scale_scores |
| Simulate Datasets Based on a Model | pool_sim_data print.sim_data sim_data |
| Create a 'sim_out' Object | print.sim_out sim_out |
| Summarize Test Results | print.test_out_list print.test_summary print.test_summary_list summarize_tests |
| Summarize 'x_from_power' Results | print.summary.n_region_from_power print.summary.x_from_power summary.n_region_from_power summary.x_from_power |
| Test a Conditional Indirect Effect | test_cond_indirect |
| Test Several Conditional Indirect Effects | test_cond_indirect_effects |
| Test Group Constraints | test_group_equal |
| Test a Moderated Mediation Effect | test_index_of_mome |
| Test an Indirect Effect | test_indirect_effect |
| Test Several Indirect Effects | test_k_indirect_effects |
| Test All Moderation Effects | test_moderation |
| Test All Free Parameters | find_par_names test_parameters |
| Sample Size and Effect Size Determination | arg_x_from_power n_from_power n_region_from_power pba_diagnosis print.n_region_from_power print.x_from_power x_from_power |
