vis_anova: ANOVA or Welch's ANOVA with appropriate post-hoc tests

In visStatistics: Automated Selection and Visualisation of Statistical Hypothesis Tests

ANOVA or Welch's ANOVA with appropriate post-hoc tests

Description

Internal function that performs ANOVA or Welch's one-way test and corresponding post-hoc comparisons. Uses TukeyHSD for equal variances (Fisher's ANOVA) and Games-Howell for unequal variances (Welch's ANOVA).

Usage

vis_anova(
  samples,
  fact,
  conf.level = conf.level,
  samplename = "",
  factorname = "",
  cex = 1
)

Arguments

samples	Numeric vector; the dependent variable.
fact	Factor; the grouping variable.
conf.level	Numeric; confidence level for tests and intervals (default: 0.95).
samplename	Character; label for y-axis (default: "").
factorname	Character; label for x-axis (default: "").
cex	Numeric; character expansion factor for plot elements (default: 1).

Details

The function first tests for homogeneity of variance using Levene's test. If variances are equal (p > alpha), Fisher's one-way ANOVA with Tukey's HSD post-hoc is performed. If variances are unequal (p <= alpha), Welch's one-way ANOVA with Games-Howell post-hoc is performed.

The function produces a box plot with jittered points and group means (red diamonds for the parametric branches), annotated with a compact letter display showing which groups differ significantly.

Value

A list with components:

summary statistics of ANOVA

Summary of Fisher's ANOVA or Welch's oneway test

post-hoc analysis

TukeyHSD object or Games-Howell results in compatible format

conf.level

The confidence level used

Examples

# Example with equal variances (uses Fisher's ANOVA + TukeyHSD)
data(PlantGrowth)
result1 <- vis_anova(PlantGrowth$weight, PlantGrowth$group, 
                     samplename = "Weight", factorname = "Group")

# Example with unequal variances (uses Welch's ANOVA + Games-Howell)
# Create data with heterogeneous variances
set.seed(123)
group_a <- rnorm(20, mean = 10, sd = 1)
group_b <- rnorm(20, mean = 15, sd = 5)  # Much larger variance
group_c <- rnorm(20, mean = 12, sd = 2)
values <- c(group_a, group_b, group_c)
groups <- factor(rep(c("A", "B", "C"), each = 20))
result2 <- vis_anova(values, groups, 
                     samplename = "Value", factorname = "Group")

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