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()