Package index • power4mome

Main Functions

All-in-one functions for common tasks.

power4test() print(<power4test>): Estimate the Power of a Test

power4test_by_n() c(<power4test_by_n>) as.power4test_by_n() print(<power4test_by_n>): Power By Sample Sizes

power4test_by_es() c(<power4test_by_es>) as.power4test_by_es() print(<power4test_by_es>): Power By Effect Sizes

x_from_power() n_from_power() n_region_from_power() print(<x_from_power>) print(<n_region_from_power>): Sample Size and Effect Size Determination

Helper Functions

Helpers for main functions

rejection_rates() print(<rejection_rates_df>): Rejection Rates

plot(<x_from_power>) plot(<n_region_from_power>): Plot The Results of 'x_from_power'

summary(<x_from_power>) summary(<n_region_from_power>) print(<summary.x_from_power>) print(<summary.n_region_from_power>): Summarize 'x_from_power' Results

power_curve() print(<power_curve>): Power Curve

plot(<power_curve>) plot(<power4test_by_n>) plot(<power4test_by_es>): Plot a Power Curve

predict(<power_curve>): Predict Method for a 'power_curve' Object

Test Functions

Built-in functions for do common tests such as testing indirect effects and model parameters.

test_indirect_effect(): Test an Indirect Effect

test_k_indirect_effects(): Test Several Indirect Effects

test_cond_indirect(): Test a Conditional Indirect Effect

test_cond_indirect_effects(): Test Several Conditional Indirect Effects

test_moderation(): Test All Moderation Effects

test_index_of_mome(): Test a Moderated Mediation Effect

test_parameters() find_par_names(): Test All Free Parameters

Advanced Functions

For advanced users to do the power analysis step-by-step or build a customized workflow.

ptable_pop() model_matrices_pop(): Generate the Population Model

sim_data() print(<sim_data>) pool_sim_data(): Simulate Datasets Based on a Model

fit_model(): Fit a Model to a List of Datasets

gen_mc(): Generate Monte Carlo Estimates

gen_boot(): Generate Bootstrap Estimates

sim_out() print(<sim_out>): Create a 'sim_out' Object

do_test(): Do a Test on Each Replication

Generate Random Numbers

Functions for generating one variable from a specific distribution.

rbeta_rs(): Random Variable From a Beta Distribution

rbeta_rs2(): Random Variable From a Beta Distribution (User Range)

rbinary_rs(): Random Binary Variable

rexp_rs(): Random Variable From an Exponential Distribution

rlnorm_rs(): Random Variable From a Lognormal Distribution

rpgnorm_rs(): Random Variable From a Generalized Normal Distribution

rt_rs(): Random Variable From a t Distribution

runif_rs(): Random Variable From a Uniform Distribution

Miscellaneous Functions

Miscellaneous functions

summarize_tests() print(<test_summary_list>) print(<test_summary>) print(<test_out_list>): Summarize Test Results

pop_es_yaml(): Parse YAML-Stye Values For 'pop_es'