Simulation-Based Power Analysis for Mixed-Effects Models
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Documentation for package ‘mixpower’ version 1.1.1
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 |