norm_tests: Normality Tests and Plots

Description

Performs normality tests and creates related plots (histogram, density, Q-Q). This is primarily a subfunction of t_neat and anova_neat, but here it is available separately for other potential purposes.

Usage

norm_tests(
  var1,
  var2 = NULL,
  pair = FALSE,
  norm_tests = "all",
  alpha = 0.05,
  plots = FALSE,
  aspect_ratio = 1,
  hush = FALSE
)

Value

Prints normality tests, and displays plots if so specified. Returns

TRUE if any of the specified tests has p value below the specified

alpha, otherwise returns FALSE.

Arguments

var1: Numeric vector; numbers of any given variable.
var2: Optional numeric vector (or NULL); numbers of a second variable.
pair: Logical; only matters if var2 is not null. In that case, if TRUE each normality test is performed for the difference values between the two variables in case of paired samples, or, if FALSE, separately for each of the two variables for unpaired samples.
norm_tests: Normality tests. Any or all of the following character input is accepted (as a single string or a character vector; case-insensitive): "W" (Shapiro-Wilk), "K2" (D'Agostino), "A2" (Anderson-Darling), "JB" (Jarque-Bera); see Notes. The option "all" (default value) selects all four previous tests at the same time.
alpha: Numeric (.05 by default), alpha level: if any p value if below this alpha level, the function returns TRUE, otherwise FALSE.
plots: Logical: if TRUE adds histogram, density, and Q-Q plots. (Note: in case of paired samples, Q-Q plots are plotted on a separate figure. In RStudio, press on "Previous plot" under "Plots" to see these Q-Q plots.)
aspect_ratio: Aspect ratio of the plots: 1 (1/1) by default. (Set to NULL for dynamic aspect ratio.)
hush: Logical. If TRUE, prevents printing any details to console.

References

Lakens, D. (2015). The perfect t-test (version 1.0.0). Retrieved from https://github.com/Lakens/perfect-t-test. tools:::Rd_expr_doi("https://doi.org/10.5281/zenodo.17603")

Yap, B. W., & Sim, C. H. (2011). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation, 81(12), 2141–2155. tools:::Rd_expr_doi("https://doi.org/10.1080/00949655.2010.520163")

Examples

Run this code


norm_tests(stats::rnorm(100))
# should be normal...