Factorial Designs — pamlfactorial • pamlj

Power analysis for between-subjects, within-subjects, and mixed factorial designs specified via observed or expected cell means and standard deviations.

pamlfactorial(
  data,
  aim = "n",
  mode = "facpeta",
  means = NULL,
  sds = NULL,
  factors = NULL,
  within = NULL,
  r = 0,
  peta = 0.2,
  effect_type,
  repeated_type,
  df_effect = 1,
  effect_groups = 2,
  design_groups = 2,
  power = 0.9,
  n = 20,
  sig.level = 0.05,
  plot_contour = FALSE,
  plot_escurve = FALSE,
  plot_ncurve = FALSE,
  plot_log = FALSE,
  plot_palette = "viridis",
  plot_x = "none",
  plot_y = "none",
  plot_custom_labels = FALSE,
  plot_z = "none",
  plot_x_from = 0,
  plot_x_to = 0,
  plot_z_lines = 0,
  plot_z_value = list(),
  plot_to_table = FALSE,
  ncp_type,
  emeans = FALSE,
  esos = FALSE,
  .interface = "jamovi",
  .caller = "factorial"
)

Arguments

data: the data as a data frame
aim: The aim of the analysis: n (default) for sample size, power to estimate power
mode: .
means: a string naming the variable containing the means from data; the variable must be numeric.
sds: a string naming the variable containing the standard deviations from data; the variable must be numeric.
factors: a vector of strings naming the factors from data. Not needed if formula is used.
within: a vector of strings naming the repeated measures factors from data.
r: Expected correlation among repeated measures
peta: .
effect_type: .
repeated_type: .
df_effect: .
effect_groups: .
design_groups: .
power: Minimal desired power
n: Sample size
sig.level: Type I error rate (significance cut-off or alpha)
plot_contour: .
plot_escurve: .
plot_ncurve: .
plot_log: .
plot_palette: .
plot_x: .
plot_y: .
plot_custom_labels: .
plot_z: .
plot_x_from: .
plot_x_to: .
plot_z_lines: .
plot_z_value: .
plot_to_table: .
ncp_type: .
emeans: .
esos: .
.interface: Used for internal purposes
.caller: .

Value

A results object containing:

`results$intro`					a html
`results$extrainfo`					a html
`results$issues`					a html
`results$infotab`					a table
`results$powertab`					a table
`results$powerbyes`					a table
`results$means`					an array of predicted means tables
`results$plotnotes`					a html
`results$powerContour`					an image
`results$powerEscurve`					an image
`results$powerNcurve`					an image
`results$powerCustom`					an image
`results$customnotes`					a html
`results$customtable`					a table

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$infotab$asDF

as.data.frame(results$infotab)