p-value from Shapiro-Wilk Normality Test simulation

Generates univariate distributional data and returns a p-value to assess the null that the population follows a Gaussian distribution shape. Uses shapiro.test.

Usage

p_shapiro.test(dist, return_analysis = FALSE)

Arguments

dist

expression used to generate the required sample data

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

# 50 observations drawn from normal distribution (null is true)
p_shapiro.test(rnorm(50))
#> [1] 0.8359497

# return analysis object
p_shapiro.test(rnorm(50), TRUE)
#> 
#> 	Shapiro-Wilk normality test
#> 
#> data:  dist
#> W = 0.98676, p-value = 0.8438
#> 

# 50 observations from slightly skewed chi-squared distribution (power)
p_shapiro.test(rchisq(50, df=100))
#> [1] 0.9303694

# \donttest{
    # empirical Type I error rate estimate
    p_shapiro.test(rnorm(50)) |> Spower()
#> Warning: number of items to replace is not a multiple of replacement length
#> 
#> ── Spower Results ──────────────────────────────────────────────────────────────
#> 
#> Design conditions:
#> 
#> # A tibble: 1 × 3
#>      dist sig.level power
#>     <dbl>     <dbl> <lgl>
#> 1 -1.3847      0.05 NA   
#> 
#> Estimate of power: 0.047
#> 95% Confidence Interval: [0.043, 0.051]
#> Execution time (H:M:S): 00:00:02

    # power
    p_shapiro.test(rchisq(50, df=100)) |> Spower()
#> Warning: number of items to replace is not a multiple of replacement length
#> 
#> ── Spower Results ──────────────────────────────────────────────────────────────
#> 
#> Design conditions:
#> 
#> # A tibble: 1 × 3
#>     dist sig.level power
#>    <dbl>     <dbl> <lgl>
#> 1 84.276      0.05 NA   
#> 
#> Estimate of power: 0.103
#> 95% Confidence Interval: [0.097, 0.109]
#> Execution time (H:M:S): 00:00:02
# }