Visualisation of the normality distribution of the standardised residuals

Source: R/vis_anova_assumptions.R

Checks for normality of the standardised residuals in ANOVA or regression. Performs the Shapiro-Wilk test and Anderson-Darling test for normality and, if not a regression, also the Levene-Brown-Forsythe and the Bartlett's test for homogeneity of variances. It produces a histogram with normal overlay, a residuals vs fitted plot, and a normal Q-Q plot.

vis_anova_assumptions(
  samples,
  fact,
  conf.level = 0.95,
  cex = 1,
  regression = FALSE
)

Arguments

samples

Numeric vector; the dependent variable.

fact

Factor; the independent variable.

conf.level

Numeric; confidence level for the tests (default: 0.95).

cex

Numeric; scaling factor for plot text and symbols (default: 1).

regression

Logical; if TRUE, skips Bartlett's test (for regression diagnostics). Default is FALSE.

Value

A list with elements:

summary_anova

Summary of the ANOVA model.

shapiro_test

Result from shapiro.test().

ad_test

Result from nortest::ad.test() or a character message if n < 7.

levene_test

Result from levene.test() (only if regression = FALSE).

bartlett_test

Result from bartlett.test() (only if regression = FALSE).

Examples

ToothGrowth$dose <- as.factor(ToothGrowth$dose)
vis_anova_assumptions(ToothGrowth$len, ToothGrowth$dose)

#> $summary_anova
#>             Df Sum Sq Mean Sq F value   Pr(>F)    
#> fact         2   2426    1213   67.42 9.53e-16 ***
#> Residuals   57   1026      18                     
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> $shapiro_test
#> 
#> 	Shapiro-Wilk normality test
#> 
#> data:  std_residuals
#> W = 0.96731, p-value = 0.1076
#> 
#> 
#> $ad_test
#> 
#> 	Anderson-Darling normality test
#> 
#> data:  std_residuals
#> A = 0.68679, p-value = 0.06928
#> 
#> 
#> $levene_test
#> 
#> 	Levene-Brown-Forsythe Test (center = median)
#> 
#> data:  absolute deviations from group medians (for ANOVA on spread differences)
#> F = 0.64573, df1 = 2, df2 = 57, p-value = 0.5281
#> 
#> 
#> $bartlett_test
#> 
#> 	Bartlett test of homogeneity of variances
#> 
#> data:  samples by fact
#> Bartlett's K-squared = 0.66547, df = 2, p-value = 0.717
#> 
#> 
#> attr(,"class")
#> [1] "vis_anova_assumptions"