Simulation-Based Power Analysis for Mixed-Effects Models

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Documentation for package ‘mixpower’ version 1.1.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

mixpower-package	Simulation-Based Power Analysis for Mixed-Effects Models
as_tibble.mp_power	Coerce mixpower results to a tibble
autoplot.mp_sensitivity	ggplot2 diagnostic plot for sensitivity or power curve
effect_size	Effect-size converters for eliciting assumptions
fit_model	Fit a model for a single simulated dataset
mixpower	Simulation-Based Power Analysis for Mixed-Effects Models
mp_assumptions	Create modeling assumptions for simulation-based power
mp_backend	MixPower backend contract
mp_backend_glmmtmb	Build a glmmTMB backend for Gaussian LMM scenarios
mp_backend_lme4	Build an lme4 backend for MixPower scenarios
mp_backend_lme4_binomial	Build an lme4 backend for binomial GLMM scenarios
mp_backend_lme4_nb	Build an lme4 backend for Negative Binomial GLMM scenarios
mp_backend_lme4_poisson	Build an lme4 backend for Poisson GLMM scenarios
mp_beta_to_d	Effect-size converters for eliciting assumptions
mp_beta_to_r2	Effect-size converters for eliciting assumptions
mp_bundle_results	Bundle results with manifest and optional labels
mp_calibrate	Check the Type I error calibration of a scenario's test
mp_compare_models	Compare analysis models on the same simulated data
mp_design	Create a study design specification
mp_d_to_beta	Effect-size converters for eliciting assumptions
mp_extend	Scale a fitted-model scenario's sample size up or down
mp_from_fit	Build a power scenario from a fitted lme4 model
mp_f_to_beta	Effect-size converters for eliciting assumptions
mp_grid_sample_size	Create a grid of values for sample-size search
mp_icc_to_sd	Effect-size converters for eliciting assumptions
mp_logodds_to_or	Effect-size converters for eliciting assumptions
mp_manifest	Reproducibility manifest for power analyses
mp_methods_text	Generate a methods paragraph for a power analysis
mp_missing	Add a missing-data / dropout mechanism to a scenario
mp_or_to_logodds	Effect-size converters for eliciting assumptions
mp_power	Simulation-based power estimation (engine-agnostic core)
mp_power_checkpoint	Resumable, checkpointed power simulation
mp_power_curve	Power curve for a single design/assumption parameter
mp_power_curve_parallel	Parallel power curve evaluation
mp_quick_power	Quick power run for a single LMM design
mp_r2_to_beta	Effect-size converters for eliciting assumptions
mp_recommend_method	Recommend an inference method for a scenario
mp_report_table	Publication-ready summary table for power results
mp_safeguard_effect	Safeguard (confidence-bound) effect size from a fitted model
mp_scenario	Create a power-analysis scenario
mp_scenario_glmmtmb_lmm	Gaussian LMM scenario using glmmTMB
mp_scenario_lme4	Create a fully specified MixPower scenario with the lme4 backend
mp_scenario_lme4_binomial	Create a fully specified MixPower scenario with the binomial lme4 backend
mp_scenario_lme4_nb	Create a fully specified MixPower scenario with the NB lme4 backend
mp_scenario_lme4_poisson	Create a fully specified MixPower scenario with the Poisson lme4 backend
mp_sd_to_icc	Effect-size converters for eliciting assumptions
mp_sensitivity	Run power sensitivity analysis over a parameter grid
mp_sensitivity_parallel	Parallel sensitivity analysis over a parameter grid
mp_sesoi	Set a smallest effect size of interest (SESOI) on a scenario
mp_solve_sample_size	Solve for minimum sample size achieving target power
mp_t_to_beta	Effect-size converters for eliciting assumptions
mp_write_results	Write results or bundle to CSV or JSON
plot.mp_power	Plot the p-value distribution of a power analysis
plot.mp_power_curve	Plot a power curve
plot.mp_sensitivity	Plot a sensitivity analysis
plot_power	Plot power results
run_parallel	Placeholder for parallel execution
simulate_glmm_binomial_data	Simulate binary outcome data for a GLMM with random effects
simulate_glmm_nb_data	Simulate count outcome data for a Negative Binomial GLMM with random effects
simulate_glmm_poisson_data	Simulate count outcome data for a Poisson GLMM with random effects
simulate_power	Run a simple simulation-based power study
summarize_simulations	Summarize simulation outputs
test_effect	Extract a test statistic for a model term
validate_mp_backend	Validate a MixPower backend