pj_pow {mmirestriktor}

Power Calculation for ANOVA Simulation

Description

This function calculates the power for hypothesis tests in a constrained statistical inference setting, particularly in the context of ANOVA and regression as discussed in Vanbrabant et al. (2015). It is designed to work with a list of data frames, where each data frame represents a different dataset. The function accommodates both equality and inequality constraints.

Usage

pj_pow(df_list, constr = 0, alpha = 0.05)

Arguments

df_list

A list of data frames, each representing a dataset. Designed to use results generated from the generate_datasets() function.

constr

An integer indicating the number of inequality constraints. A value of 0 indicates that all constraints are equality constraints. The value must be a non-negative integer less than the number of groups.

alpha

The significance level used in the hypothesis testing, with a default value of 0.05. It should be a numeric value between 0 and 1.

Details

The function first checks the validity of the 'constr' parameter and then constructs the constraint string based on the number of constraints. It runs the model for each dataset in the df_list using the mmir_model function and applies the constraints using the restriktor::iht function. The power is calculated based on the proportion of datasets that meet the hypothesis test criteria defined by the constraints and the significance level.

Value

The function returns the calculated power as a numeric value, representing the proportion of p-values smaller than the predefined significance level alpha.

References

Vanbrabant, Leonard; Van De Schoot, Rens; Rosseel, Yves (2015). Constrained statistical inference: sample-size tables for ANOVA and regression. Frontiers in Psychology, 5. DOI:10.3389/fpsyg.2014.01565. URL: https://www.frontiersin.org/articles/10.3389/fpsyg.2014.01565

Examples

generate_datasets(S = 2, k = 4, f = 0.25, n = 30) |> pj_pow(constr=1)