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

## Description

Simulation function used to perform the simulation

## Usage

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

## Arguments

 `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)

## Value

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.

## References

 ``` 1 2 3 4 5 6 7 8 9 10``` ```## 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) ```