# ANOVA_compromise: Justify your alpha level by minimizing or balancing Type 1... In Superpower: Simulation-Based Power Analysis for Factorial Designs

 ANOVA_compromise R Documentation

## Justify your alpha level by minimizing or balancing Type 1 and Type 2 error rates for ANOVAs.

### Description

Justify your alpha level by minimizing or balancing Type 1 and Type 2 error rates for ANOVAs.

### Usage

```ANOVA_compromise(
design_result,
correction = Superpower_options("correction"),
emm = Superpower_options("emm"),
emm_model = Superpower_options("emm_model"),
contrast_type = Superpower_options("contrast_type"),
emm_comp,
costT1T2 = 1,
priorH1H0 = 1,
error = "minimal",
liberal_lambda = Superpower_options("liberal_lambda")
)
```

### Arguments

 `design_result` Output from the ANOVA_design function `correction` Set a correction of violations of sphericity. This can be set to "none", "GG" Greenhouse-Geisser, and "HF" Huynh-Feldt `emm` Set to FALSE to not perform analysis of estimated marginal means `emm_model` Set model type ("multivariate", or "univariate") for estimated marginal means `contrast_type` Select the type of comparison for the estimated marginal means. Default is pairwise. See ?emmeans::'contrast-methods' for more details on acceptable methods. `emm_comp` Set the comparisons for estimated marginal means comparisons. This is a factor name (a), combination of factor names (a+b), or for simple effects a | sign is needed (a|b) `costT1T2` Relative cost of Type 1 errors vs. Type 2 errors. `priorH1H0` How much more likely a-priori is H1 than H0? Default is 1: equally likely. `error` Either "minimal" to minimize error rates, or "balance" to balance error rates. `liberal_lambda` Logical indicator of whether to use the liberal (cohen_f^2\*(num_df+den_df)) or conservative (cohen_f^2\*den_df) calculation of the noncentrality (lambda) parameter estimate. Default is FALSE.

### Value

Returns dataframe with simulation data (power and effect sizes!), optimal alpha level, obtained beta error rate (1-power/100), and objective (see below for details). If NA is obtained in a alpha/beta/objective columns this indicates there is no effect for this particular comparison. Also returns alpha-beta compromise plots for all comparisons. Note: Cohen's f = sqrt(pes/1-pes) and the noncentrality parameter is = f^2*df(error)

`"aov_comp"`

A dataframe of ANOVA-level results.

`"aov_plotlist"`

List of plots for ANOVA-level effects

`"manova_comp"`

A dataframe of MANOVA-level results.

`"manova_plotlist"`

List of plots for MANOVA-level effects.

`"emmeans_comp"`

A dataframe of ANOVA-level results.

`"emm_plotlist"`

List of plots for estimated marginal means contrasts.

alpha = alpha or Type 1 error that minimizes or balances combined error rates beta = beta or Type 2 error that minimizes or balances combined error rates objective = value that is the result of the minimization, either 0 (for balance) or the combined weighted error rates

### Examples

```## Not run:
design_result <- ANOVA_design(design = "3b*2w",
n = 6,
mu = c(1, 2, 2, 3, 3, 4),
sd = 3,
plot = FALSE)
example = ANOVA_compromise(design_result,emm = TRUE,emm_comp = "a")

## End(Not run)
```

Superpower documentation built on May 17, 2022, 5:08 p.m.