generate_datasets_reg {mmirestriktor}

Generate Multiple Datasets for Regression Simulation

Description

This function generates a specified number of datasets for regression analysis simulations. Each dataset is generated using the sim_reg function, based on given parameters like sample size, number of predictors, effect size, and correlation coefficient.

Usage

generate_datasets_reg(S = 20000, n, p, f2, rho, beta = 0.1)

Arguments

S

The number of datasets to generate, default is 20000.

n

The number of observations in each dataset.

p

The number of predictors in the regression model for each dataset.

f2

The effect size for each dataset, defined as (f^2 = R^2 / (1 - R^2)).

rho

The correlation coefficient between predictors in each dataset.

beta

The regression coefficients for the predictors in each dataset, either as a single value or a vector of length (p).

Details

The function uses sim_reg to simulate individual datasets, which are then combined into a list. Each dataset is a data frame with named columns for the response variable and predictors.

Value

A list of data frames, each representing a simulated dataset for regression analysis. Each data frame contains columns for the response variable 'y' and predictors 'x1', 'x2', ..., 'xp'.

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

datasets <- generate_datasets_reg(S = 2, n = 50, p = 3, f2 = 0.10, rho = 0.5)