replext_ts3_c4.2 {npboottprm}

Replicate and Extend Statistical Power Analysis for ANOVA with Specific Sample Size Combinations

Description

This function is a wrapper around replext_ts2_c1.1. The function is designed to reproduce or extend the statistical power analysis for ANOVA (Analysis of Variance) from Dwivedi et al. (2017) supplemental table 3, cell 4.2.

Usage

replext_ts3_c4.2(
  M1 = 5,
  S1 = 1,
  M2 = 6,
  S2 = 2,
  M3 = 7,
  S3 = 4,
  Sk1 = 0.8,
  Sk2 = 0.8,
  Sk3 = 0.8,
  n1 = c(2, 2, 2, 3, 2, 2, 3, 2, 3, 2),
  n2 = c(2, 3, 3, 4, 2, 3, 4, 2, 4, 2),
  n3 = c(3, 3, 4, 3, 6, 6, 4, 7, 5, 8),
  n_simulations = 10000,
  nboot = 1000,
  conf.level = 0.95
)

Arguments

M1

Mean for the first group, default is 5.

S1

Standard deviation for the first group, default is 1.

M2

Mean for the second group, default is 6.

S2

Standard deviation for the second group, default is 2.

M3

Mean for the third group, default is 7.

S3

Standard deviation for the third group, default is 4.

Sk1

Skewness parameter for the first group, default is 0.8.

Sk2

Skewness parameter for the second group, default is 0.8.

Sk3

Skewness parameter for the third group, default is 0.8.

n1

Vector of sample sizes for the first group, with specific combinations.

n2

Vector of sample sizes for the second group, with specific combinations.

n3

Vector of sample sizes for the third group, with specific combinations.

n_simulations

Number of simulations to run, default is 10,000.

nboot

Number of bootstrap samples for the nonparametric bootstrap test, default is 1000.

conf.level

Confidence level for calculating p-value thresholds, default is 0.95.

Value

A data frame with columns for each sample size combination (n1, n2, n3) and the proportions of significant p-values for each test (ANOVA, Kruskal-Wallis, Nonparametric Bootstrap F-test, Permutation F-test).

References

Dwivedi AK, Mallawaarachchi I, Alvarado LA. Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method. Stat Med. 2017 Jun 30;36(14):2187-2205. doi: 10.1002/sim.7263. Epub 2017 Mar 9. PMID: 28276584.

See Also

replext_ts2_c1.1

Examples

replext_ts3_c4.2(n1 = c(10), n2 = c(10), n3 = c(10), n_simulations = 1)