View source: R/replext_table_cells.R
| replext_t1_c1 | R Documentation | 
This function performs repeated simulations for ANOVA to determine minimum sample sizes for given power and effect sizes, as well as calculating Type I error rates. It is designed to replicate and extend the results for Table 1 Cell 1 in Vanbrabant et al. (2015).
replext_t1_c1(
  S = 20000,
  k = 3,
  fs = c(0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4),
  n_start = 6,
  constrs = c(0, 1, 2),
  alpha = 0.05,
  pow = 0.8,
  nmax = 1000
)
S | 
 The number of datasets to generate for each simulation, default is 20000.  | 
k | 
 The number of groups in the ANOVA design.  | 
fs | 
 A vector of effect sizes to consider in the simulations.  | 
n_start | 
 The starting sample size for the simulations.  | 
constrs | 
 A vector of constraint types to be used in the simulations.  | 
alpha | 
 The significance level used in hypothesis testing, default is 0.05.  | 
pow | 
 The desired power for the statistical test, default is 0.80.  | 
nmax | 
 The maximum sample size to consider in the simulations.  | 
The function uses a nested approach, first determining minimum sample sizes for various combinations of effect size and constraints, and then calculating Type I error rates. It leverages the 'pj_pow' function for power calculation and integrates internal function 'find_min_sample_size' for determining the smallest sample size achieving the desired power.
A data frame containing the calculated Type I error rates and the minimum sample sizes required for each combination of effect size and constraint type.
Vanbrabant, Leonard; Van De Schoot, Rens; Rosseel, Yves (2015). Constrained statistical inference: sample-size tables for ANOVA and regression. Frontiers in Psychology, 5. DOI:10.3389/fpsyg.2014.01565. URL: https://www.frontiersin.org/articles/10.3389/fpsyg.2014.01565
replext_t1_c1(S=5, fs = c(0.40), constrs = c(2))
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