power_lm {Keng}R Documentation

Compute the post-hoc power and/or plan the sample size for one or a set of predictors in linear regression

Description

Compute the post-hoc power and/or plan the sample size for one or a set of predictors in linear regression

Usage

power_lm(PRE = 0.02, PC = 0, PA = 1, power = 0.8, sig.level = 0.05, n = NULL)

Arguments

PRE

Proportional Reduction in Error. PRE = The square of partial correlation. Cohen (1988) suggested >=0.02, >=0.13, and >=0.26 as cut-off values of PRE for small, medium, and large effect sizes, respectively.

PC

Number of parameters of model C (compact model) without focal predictors of interest.

PA

Number of parameters of model A (augmented model) with focal predictors of interest.

power

Expected statistical power for effects of focal predictors.

sig.level

Expected significance level for effects of focal predictors.

n

The current sample size. If n is given, the post-hoc power would be computed.

Value

A list with 4 items: (1) post, the post-hoc F-test, lambda (non-centrality parameter), and power for sample size n; (2)minimum, the minimum sample size required for focal predictors to reach the expected statistical power and significance level; (3) prior, a data.frame including n_i, PC, PA,df_A_i, F_i, p_i, lambda_i, power_i. ⁠_i⁠ indicates these statistics are the intermediate iterative results. Each row of prior presents results for one possible sample size n_i. Given n_i, df_A_i, F_i, p_i, lambda_i and power_i would be computed accordingly. (4) A plot of power against sample size n. The cut-off value of n for expected statistical power power and expected significance level sig.level is annotated on the plot.

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.

Examples

power_lm()

[Package Keng version 2024.11.25 Index]