vis_lm_assumptions: Visualisation of the normality distribution of the...

In visStatistics: Automated Selection and Visualisation of Statistical Hypothesis Tests

Visualisation of the normality distribution of the standardised residuals

Description

Checks for normality of the standardised residuals in the general linear model Student's t-test (t.test,var=EQUAL) Fisher oneway ANOVA (aov) or simple linear 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.

Usage

vis_lm_assumptions(samples, fact, cex = 1, regression = FALSE)

vis_anova_assumptions(...)

Arguments

samples	Numeric vector; the dependent variable.
fact	Factor; the independent variable.
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.
...	Arguments passed to vis_lm_assumptions().

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).

bp_test

Result from bp.test() (only if regression = TRUE).

Examples

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

visStatistics documentation built on May 13, 2026, 1:08 a.m.