ANOVA_exact: Simulates an exact dataset (mu, sd, and r represent...
In Superpower: Simulation-Based Power Analysis for Factorial Designs

ANOVA_exact

R Documentation

Simulates an exact dataset (mu, sd, and r represent empirical, not population, mean and covariance matrix) from the design to calculate power

Description

Simulates an exact dataset (mu, sd, and r represent empirical, not population, mean and covariance matrix) from the design to calculate power

Usage

ANOVA_exact(
  design_result,
  correction = Superpower_options("correction"),
  alpha_level = Superpower_options("alpha_level"),
  verbose = Superpower_options("verbose"),
  emm = Superpower_options("emm"),
  emm_model = Superpower_options("emm_model"),
  contrast_type = Superpower_options("contrast_type"),
  liberal_lambda = Superpower_options("liberal_lambda"),
  emm_comp
)

ANOVA_exact2(
  design_result,
  correction = Superpower_options("correction"),
  alpha_level = Superpower_options("alpha_level"),
  verbose = Superpower_options("verbose"),
  emm = Superpower_options("emm"),
  emm_model = Superpower_options("emm_model"),
  contrast_type = Superpower_options("contrast_type"),
  emm_comp,
  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
`alpha_level`	Alpha level used to determine statistical significance
`verbose`	Set to FALSE to not print results (default = TRUE)
`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.
`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.
`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)

Value

Returns dataframe with simulation data (power and effect sizes!), anova results and simple effect results, plot of exact data, and alpha_level. Note: Cohen's f = sqrt(pes/1-pes) and the noncentrality parameter is = f^2*df(error)

"dataframe": A dataframe of the simulation result.
"aov_result": aov object returned from aov_car.
"aov_result": emmeans object returned from emmeans.
"main_result": The power analysis results for ANOVA level effects.
"pc_results": The power analysis results for the pairwise (t-test) comparisons.
"emm_results": The power analysis results of the pairwise comparison results.
"manova_results": Default is "NULL". If a within-subjects factor is included, then the power of the multivariate (i.e. MANOVA) analyses will be provided.
"alpha_level": The alpha level, significance cut-off, used for the power analysis.
"method": Record of the function used to produce the simulation
"plot": A plot of the dataframe from the simulation; should closely match the meansplot in ANOVA_design

Functions

ANOVA_exact2: An extension of ANOVA_exact that uses the effect sizes calculated from very large sample size empirical simulation. This allows for small sample sizes, where ANOVA_exact cannot, while still accurately estimating power. However, model objects (emmeans and aov) are not included as output, and pairwise (t-test) results are not currently supported.

Warnings

Varying the sd or r (e.g., entering multiple values) violates assumptions of homoscedascity and sphericity respectively

Examples

## 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"))
exact_result <- ANOVA_exact(design_result, alpha_level = 0.05)

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