One-way comparison with automatic method selection

Performs a one-way ANOVA, Welch ANOVA, or Kruskal-Wallis test with automatic assumption checking and optional post-hoc comparisons.

Usage

quick_anova(
  data,
  group_col,
  value_col,
  method = c("auto", "anova", "welch", "kruskal"),
  post_hoc = c("auto", "none", "tukey", "welch", "wilcox"),
  alpha = 0.05
)

# S3 method for class 'quick_anova_result'
plot(
  x,
  y = NULL,
  plot_type = c("boxplot", "violin", "both"),
  add_jitter = TRUE,
  point_size = 2,
  point_alpha = 0.6,
  show_p_value = TRUE,
  p_label = c("p.format", "p.signif"),
  palette = "qual_vivid",
  ...
)

Arguments

data: A data frame.
group_col: Character. Column name for the grouping factor (2 or more levels).
value_col: Character. Column name for the numeric response variable.
method: One of "auto" (default), "anova", "welch", or "kruskal".
post_hoc: One of "auto" (default), "none", "tukey", "welch", or "wilcox".
alpha: Numeric. Significance level. Default 0.05.
x: A quick_anova_result object from quick_anova().
y: Ignored.
plot_type: One of "boxplot" (default), "violin", "both".
add_jitter: Logical. Add jittered points? Default TRUE.
point_size: Numeric. Jitter point size. Default 2.
point_alpha: Numeric. Jitter point transparency (0-1). Default 0.6.
show_p_value: Logical. Annotate plot with omnibus p-value? Default TRUE.
p_label: One of "p.format" (default) or "p.signif" (stars).
palette: evanverse palette name. Default "qual_vivid". NULL uses ggplot2 defaults.
...: Additional arguments passed to the internal plotting backend.

Value

An object of class "quick_anova_result" (invisibly) containing:

omnibus_result: List with test object, p-value, and effect size
post_hoc: Post-hoc table (or NULL if none)
method_used: Character: "anova", "welch", or "kruskal"
descriptive_stats: Per-group summary data frame
assumption_checks: List with normality and variance test results (diagnostics even when method is forced)
params: List of input parameters
data: Cleaned data frame (for plot() method)

Use print(result) for a brief summary, summary(result) for full details, and plot(result) for a comparison plot.

Details

Auto method selection logic:

Normality checked per group via Shapiro-Wilk (sample-size-adaptive thresholds — see quick_ttest for details).
If normality passes: Levene's test decides between classical ANOVA (equal variances) and Welch ANOVA (unequal variances).
If normality fails: Kruskal-Wallis is used.

Post-hoc defaults when post_hoc = "auto":

"anova" → Tukey HSD
"welch" → Pairwise Welch t-tests (BH-adjusted)
"kruskal" → Pairwise Wilcoxon (BH-adjusted)

Examples

set.seed(123)
df <- data.frame(
  group = rep(LETTERS[1:3], each = 40),
  value = rnorm(120, mean = rep(c(0, 0.5, 1.2), each = 40), sd = 1)
)
result <- quick_anova(df, group_col = "group", value_col = "value")
print(result)
#> One-way ANOVA | p < 0.001* | A n=40, B n=40, C n=40
summary(result)
#> 
#> ── One-way Comparison ──────────────────────────────────────────────────────────
#> 
#> ── Parameters ──
#> 
#> Test: One-way ANOVA
#> alpha: 0.050
#> 
#> ── Omnibus Test ──
#> 
#> ✔ p < 0.001  (significant at alpha = 0.05)
#> eta_squared = 0.224, omega_squared = 0.210
#> 
#>              Df Sum Sq Mean Sq F value   Pr(>F)    
#> group         2  27.51  13.755   16.91 3.53e-07 ***
#> Residuals   117  95.14   0.813                     
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> ── Descriptive statistics ──
#> 
#> # A tibble: 3 × 7
#>   group     n   mean    sd median    min   max
#>   <fct> <int>  <dbl> <dbl>  <dbl>  <dbl> <dbl>
#> 1 A        40 0.0452 0.898 0.0906 -1.97   1.79
#> 2 B        40 0.493  0.960 0.437  -1.81   2.67
#> 3 C        40 1.21   0.844 1.15   -0.468  3.39
#> ── Normality (Shapiro-Wilk) ──
#> 
#> A: n = 40, p = 0.953
#> B: n = 40, p = 0.940
#> C: n = 40, p = 0.913
#> -> Medium samples (min n = 40). Data reasonably normal (all Shapiro p ≥ 0.01).
#> 
#> ── Variance (Levene's test) ──
#> 
#> p = 0.854 | equal variances: TRUE
#> 
#> ── Post-hoc (tukey) ──
#> 
#> # A tibble: 3 × 6
#>   group2 group1  diff     lwr   upr     `p adj`
#>   <chr>  <chr>  <dbl>   <dbl> <dbl>       <dbl>
#> 1 B      A      0.448 -0.0306 0.927 0.0716     
#> 2 C      A      1.16   0.684  1.64  0.000000201
#> 3 C      B      0.715  0.236  1.19  0.00163    
#> 
plot(result)