p-value from variance test simulation

Generates one or or more sets of continuous data group-level data to perform a variance test, and return a p-value. When two-samples are investigated the var.test function will be used, otherwise functions from the EnvStats package will be used.

Usage

p_var.test(
  n,
  vars,
  n.ratios = rep(1, length(vars)),
  sigma2 = 1,
  two.tailed = TRUE,
  test = "Levene",
  correct = TRUE,
  gen_fun = gen_var.test,
  return_analysis = FALSE,
  ...
)

gen_var.test(n, vars, n.ratios = rep(1, length(vars)), ...)

Arguments

n

sample size per group, assumed equal across groups

vars

a vector of variances to use for each group; length of 1 for one-sample tests

n.ratios

allocation ratios reflecting the sample size ratios. Default of 1 sets the groups to be the same size (n * n.ratio)

sigma2

population variance to test against in one-sample test

two.tailed

logical; should a two-tailed or one-tailed test be used?

test

type of test to use in multi-sample applications. Can be either 'Levene' (default), 'Bartlett', or 'Fligner'

correct

logical; use correction when test = 'Bartlett'?

gen_fun

function used to generate the required discrete data. Object returned must be a matrix with k rows and k columns of counts. Default uses gen_var.test. User defined version of this function must include the argument ...

return_analysis

logical; return the analysis object for further extraction and customization?

...

additional arguments to be passed to gen_fun. Not used unless a customized gen_fun is defined

Value

a single p-value

See also

gen_var.test

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples

# one sample
p_var.test(100, vars=10, sigma2=9)
#> [1] 0.8698271

# return analysis object
p_var.test(100, vars=10, sigma2=9, return_analysis = TRUE)
#> $statistic
#> Chi-Squared 
#>    98.47016 
#> 
#> $parameters
#> df 
#> 99 
#> 
#> $p.value
#> [1] 0.9922601
#> 
#> $estimate
#> variance 
#> 8.951833 
#> 
#> $null.value
#> variance 
#>        9 
#> 
#> $alternative
#> [1] "two.sided"
#> 
#> $method
#> [1] "Chi-Squared Test on Variance"
#> 
#> $data.name
#> [1] "dat$DV"
#> 
#> $conf.int
#>       LCL       UCL 
#>  6.900932 12.080404 
#> attr(,"conf.level")
#> [1] 0.95
#> 
#> attr(,"class")
#> [1] "htestEnvStats"

# three sample
p_var.test(100, vars=c(10, 9, 11))
#> [1] 0.1659917
p_var.test(100, vars=c(10, 9, 11), test = 'Fligner')
#> [1] 0.3943148
p_var.test(100, vars=c(10, 9, 11), test = 'Bartlett')
#> [1] 0.6316487

# \donttest{
  # power to detect three-group variance differences
  p_var.test(n=100, vars=c(10,9,11)) |> Spower()
#> 
#> Execution time (H:M:S): 00:00:32
#> Design conditions: 
#> 
#> # A tibble: 1 × 3
#>       n sig.level power
#>   <dbl>     <dbl> <lgl>
#> 1   100      0.05 NA   
#> 
#> Estimate of power: 0.119
#> 95% Confidence Interval: [0.113, 0.125]

  # sample size per group to achieve 80% power
  p_var.test(n=interval(100, 2000), vars=c(10,9,11)) |> Spower(power=.80)
#> 
#> Execution time (H:M:S): 00:02:21
#> Design conditions: 
#> 
#> # A tibble: 1 × 3
#>       n sig.level power
#>   <dbl>     <dbl> <dbl>
#> 1    NA      0.05   0.8
#> 
#> Estimate of n: 1086.1
#> 95% Predicted Confidence Interval: [1080.6, 1091.5]
# }