norm_tests {neatStats}

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
)

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.

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.

Note

Normality tests are all calculated via fBasics::NormalityTests, selected based on the recommendation of Lakens (2015), quoting Yap and Sim (2011, p. 2153): "If the distribution is symmetric with low kurtosis values (i.e. symmetric short-tailed distribution), then the D'Agostino and Shapiro-Wilkes tests have good power. For symmetric distribution with high sample kurtosis (symmetric long-tailed), the researcher can use the JB, Shapiro-Wilkes, or Anderson-Darling test." See urlhttps://github.com/Lakens/perfect-t-test for more details.

References

Lakens, D. (2015). The perfect t-test (version 1.0.0). Retrieved from https://github.com/Lakens/perfect-t-test. doi: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. doi:10.1080/00949655.2010.520163

See Also

t_neat

Examples

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