metapower (version 0.2.2)

mpower: Compute Power for Meta-analysis

Description

Computes statistical power for summary effect sizes in meta-analysis.

Usage

mpower(
  effect_size,
  study_size,
  k,
  i2,
  es_type,
  test_type = "two-tailed",
  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'.

test_type

Character value reflecting test type: ("two-tailed" or "one-tailed").

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).

2x2 Table	Group 1	Group 2
Present	a	b
Not Present	c	d

Value

Estimated Power

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

Jackson, D., Turner, R. (2017). Power analysis for random-effects meta-analysis, Research Synthesis Methods, 8(3), 290-302 doi: https://dx.doi.org/10.1002/jrsm.1240

See Also

https://jason-griffin.shinyapps.io/shiny_metapower/

Examples

# NOT RUN {
mpower(effect_size = .2, study_size = 10, k = 10, i2 = .5, es_type = "d")

# }