homogen_power {metapower} | R Documentation |
Compute Power for Test of Homogeneity in Meta-analysis
Description
Compute statistical power for the Test of Homogeneity for meta-analysis under both fixed- and random-effects models.
Usage
homogen_power(
effect_size,
study_size,
k,
i2,
es_type,
p = 0.05,
con_table = NULL
)
Arguments
effect_size |
Numerical value of effect size. | ||||||||||
study_size |
Numerical value for number number of participants (per study). | ||||||||||
k |
Numerical value for total number of studies. | ||||||||||
i2 |
Numerical value for Heterogeneity estimate (i^2). | ||||||||||
es_type |
'Character reflecting effect size metric: 'r', 'd', or 'or'. | ||||||||||
p |
Numerical value for significance level (Type I error probability). | ||||||||||
con_table |
(Optional) Numerical values for 2x2 contingency table as a vector in the following format: c(a,b,c,d).
|
Value
Estimated Power to detect differences in homogeneity of effect sizes for fixed- and random-effects models
References
Borenstein, M., Hedges, L. V., Higgins, J. P. T. and Rothstein, H. R.(2009). Introduction to meta-analysis, Chichester, UK: Wiley.
Hedges, L., Pigott, T. (2004). The Power of Statistical Tests for Moderators in Meta-Analysis, Psychological Methods, 9(4), 426-445. doi: https://dx.doi.org/10.1037/1082-989x.9.4.426
Pigott, T. (2012). Advances in Meta-Analysis. doi: https://dx.doi.org/10.1007/978-1-4614-2278-5
See Also
https://jason-griffin.shinyapps.io/shiny_metapower/
Examples
homogen_power(effect_size = .5, study_size = 10, k = 10, i2 = .50, es_type = "d")