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
power_lm(PRE = 0.02, PC = 0, PA = 1, power = 0.8, sig.level = 0.05, n = NULL)
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. |
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.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
power_lm()