lr_pow {mmirestriktor}

Calculate Power for Linear Regression Simulations

Description

This function computes the power of hypothesis tests in a linear regression setting, considering constraints on the regression coefficients. It processes a list of data frames, each representing a different dataset, and calculates the power based on specified constraints.

Usage

lr_pow(df_list, constr = 0, standardize = TRUE, alpha = 0.05)

Arguments

df_list

A list of data frames, each representing a dataset for regression analysis. Each data frame should contain the response variable 'y' and the predictor variables 'x1', 'x2', ..., 'xp'.

constr

The number of inequality constraints imposed on the regression coefficients. It must be a non-negative integer less than or equal to the number of predictors (p). A value of 0 implies no constraints or equality constraints.

standardize

A logical value indicating whether the predictor variables should be standardized before fitting the model. Default is TRUE.

alpha

The significance level used in hypothesis testing, default is 0.05.

Details

The function validates the 'constr' parameter, optionally standardizes the predictor variables, constructs the necessary constraints, and calculates power by fitting a linear model to each dataset. It uses the 'iht' function from the 'restriktor' package to apply the constraints and evaluate the hypothesis tests.

Value

A numeric value representing the calculated power, defined as the proportion of datasets meeting the hypothesis test criteria as defined by the constraints and significance level.

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_reg(S = 4, n = 30, p = 3, f2 = 0.20, rho = 0.5) |> lr_pow()