ANOVA_power: Simulation function used to perform the simulation
In Lakens/ANOVApower: Simulation-Based Power Analysis for ANOVA Designs

Description Usage Arguments Value References Examples

Simulation function used to perform the simulation

1 2	ANOVA_power(design_result, alpha_level = 0.05, correction = "none", p_adjust = "none", nsims = 1000, seed = NULL, verbose = TRUE)

`design_result`	Output from the ANOVA_design function
`alpha_level`	Alpha level used to determine statistical significance
`correction`	Set a correction of violations of sphericity. This can be set to "none", "GG" Grennhouse-Geisser, and "HF" Huynh-Feldt
`p_adjust`	Correction for multiple comparisons
`nsims`	number of simulations to perform
`seed`	Set seed for reproducible results
`verbose`	Set to FALSE to not print results (default = TRUE)

Returns dataframe with simulation data (p-values and effect sizes), anova results and simple effect results, plots of p-value distribution, p_adjust = p_adjust, nsims, and alpha_level.

too be added

## Set up a within design with 2 factors, each with 2 levels,
## with correlation between observations of 0.8,
## 40 participants (who do all conditions), and standard deviation of 2
## with a mean pattern of 1, 0, 1, 0, conditions labeled 'condition' and
## 'voice', with names for levels of "cheerful", "sad", amd "human", "robot"
design_result <- ANOVA_design(design = "2w*2w", n = 40, mu = c(1, 0, 1, 0),
      sd = 2, r = 0.8, labelnames = c("condition", "cheerful",
      "sad", "voice", "human", "robot"))
power_result <- ANOVA_power(design_result, alpha_level = 0.05,
      p_adjust = "none", seed = 2019, nsims = 10)