p-value from Scale Test simulation — p

Simulates data given one or two parent distributions and returns a p-value testing that the scale of the type distributions are the same. Default implementation uses Gaussian distributions, however the distribution function may be modified to reflect other populations of interest. Uses ansari.test or mood.test for the analysis.

Usage

p_scale(
  n,
  scale,
  n2_n1 = 1,
  two.tailed = TRUE,
  exact = NULL,
  test = "Ansari",
  parent = function(n, ...) rnorm(n),
  ...,
  return_analysis = FALSE
)

Arguments

n: sample size per group
scale: the scale to multiply the second group by (1 reflects equal scaling)
n2_n1: sample size ratio
two.tailed: logical; use two-tailed test?
exact: a logical indicating whether an exact p-value should be computed
test: type of method to use. Can be either 'Ansari' or 'Mood'
parent: data generation function (default assumes Gaussian shape). Must be population mean centered
...: additional arguments to pass to simulation functions (if used)
return_analysis: logical; return the analysis object for further extraction and customization?

Value

a single p-value

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples


# n=30 per group,
#  Distributions Gaussian with sd=1 for first group and sd=2 for second
p_scale(30, scale=2)
#> [1] 3.110729e-05
p_scale(30, scale=2, test='Mood')
#> [1] 0.1173551

# compare chi-squared distributions
parent <- function(n, df, ...) rchisq(n, df=df) - df
p_scale(30, scale=2, parent=parent, df=3)
#> [1] 0.003431284

# \donttest{
  # empirical power of the experiments
  p_scale(30, scale=2) |> Spower()
#> 
#> Execution time (H:M:S): 00:00:23
#> Design conditions: 
#> 
#> # A tibble: 1 × 4
#>       n scale sig.level power
#>   <dbl> <dbl>     <dbl> <lgl>
#> 1    30     2      0.05 NA   
#> 
#> Estimate of power: 0.774
#> 95% Confidence Interval: [0.765, 0.782]
  p_scale(30, scale=2, test='Mood') |> Spower()
#> 
#> Execution time (H:M:S): 00:00:02
#> Design conditions: 
#> 
#> # A tibble: 1 × 5
#>       n scale test  sig.level power
#>   <dbl> <dbl> <chr>     <dbl> <lgl>
#> 1    30     2 Mood       0.05 NA   
#> 
#> Estimate of power: 0.867
#> 95% Confidence Interval: [0.860, 0.873]

  p_scale(30, scale=2, parent=parent, df=3) |> Spower()
#> 
#> Execution time (H:M:S): 00:00:23
#> Design conditions: 
#> 
#> # A tibble: 1 × 5
#>       n scale    df sig.level power
#>   <dbl> <dbl> <dbl>     <dbl> <lgl>
#> 1    30     2     3      0.05 NA   
#> 
#> Estimate of power: 0.907
#> 95% Confidence Interval: [0.901, 0.913]
  p_scale(30, scale=2, test='Mood', parent=parent, df=3) |> Spower()
#> 
#> Execution time (H:M:S): 00:00:02
#> Design conditions: 
#> 
#> # A tibble: 1 × 6
#>       n scale test     df sig.level power
#>   <dbl> <dbl> <chr> <dbl>     <dbl> <lgl>
#> 1    30     2 Mood      3      0.05 NA   
#> 
#> Estimate of power: 0.949
#> 95% Confidence Interval: [0.945, 0.954]

# }