plot_power: Convenience function to plot power across a range of sample...

In Superpower: Simulation-Based Power Analysis for Factorial Designs

Convenience function to plot power across a range of sample sizes.

Description

Convenience function to plot power across a range of sample sizes.

Usage

plot_power(
  design_result,
  alpha_level = Superpower_options("alpha_level"),
  min_n = 7,
  max_n = 100,
  desired_power = 90,
  plot = Superpower_options("plot"),
  emm = Superpower_options("emm"),
  emm_model = Superpower_options("emm_model"),
  contrast_type = Superpower_options("contrast_type"),
  emm_comp,
  verbose = Superpower_options("verbose"),
  exact2 = FALSE,
  liberal_lambda = Superpower_options("liberal_lambda")
)

Arguments

design_result	Output from the ANOVA_design function
alpha_level	Alpha level used to determine statistical significance
min_n	Minimum sample size in power curve. Cannot be less than or equal to the product of factors. E.g., if design = "2b*2b" then min_n must be at least 5 (2\*2+1=5)
max_n	Maximum sample size in power curve.
desired_power	Desired power (e.g., 80, 90). N per group will be highlighted to achieve this desired power in the plot. Defaults to 90.
plot	Should power plot be printed automatically (defaults to 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
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)
verbose	Set to FALSE to not print results (default = TRUE)
exact2	Logical indicator for which ANOVA_exact function (ANOVA_exact or ANOVA_exact2) to use in the plots. Default is FALSE which uses ANOVA_exact which has sample size limitations.
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 plot with power curves for the ANOVA, and a dataframe with the summary data.

"plot_ANOVA"

Plot of power curves from ANOVA results.

"plot_MANOVA"

Plot of power curves from MANOVA results. Returns NULL if no within-subject factors.

"plot_emm"

Plot of power curves from MANOVA results. Returns NULL if emm = FALSE.

"anova_n"

Achieved Power and Sample Size for ANOVA-level effects.

"manova_n"

Achieved Power and Sample Size for MANOVA-level effects.

"emm_n"

Achieved Power and Sample Size for estimated marginal means.

"power_df"

The tabulated ANOVA power results.

"power_df_manova"

The tabulated MANOVA power results. Returns NULL if no within-subject factors.

"power_df_emm"

The tabulated Estimated Marginal Means power results. Returns NULL if emm = FALSE.

"effect_sizes"

Effect sizes (partial eta-squared) from ANOVA results.

"effect_sizes_manova"

Effect sizes (Pillai's Trace) from MANOVA results. Returns NULL if no within-subject factors.

"effect_sizes_emm"

Effect sizes (cohen's f) estimated marginal means results. Returns NULL if emm = FALSE.

References

too be added

Examples

## Not run: 
design_result <- ANOVA_design(design = "3b",
                             n = 20,
                             mu = c(0,0,0.3),
                             sd = 1,
                             labelnames = c("condition",
                             "cheerful", "neutral", "sad"))

plot_power(design_result, min_n = 50, max_n = 70, desired_power = 90)

## End(Not run)

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