Everything print shows, plus assumption checks, effect sizes,
marginal means, post-hoc comparisons and whatever further tables the method
produces (simple slopes, sphericity, the multivariate tests and univariate
follow-ups, the canonical axes).
Arguments
- object
An
anovakit_fitobject.- digits
Number of significant digits for the printed tables. Default
4.- ...
Ignored.
- x
A
summary.anovakit_fitobject.
Value
An object of class summary.anovakit_fit, which prints the
summary. Its $fit element is object.
Examples
set.seed(1)
d <- data.frame(g = rep(c("a", "b", "c"), each = 20), y = rnorm(60))
summary(anova_welch(d, "y", "g", plots = FALSE))
#> Welch's analysis of variance
#> ----------------------------
#> Call: anova_welch(data = d, response = "y", groups = "g", plots = FALSE)
#> Observations used: 60
#>
#> Omnibus test
#> term statistic num_df den_df p_value
#> 1 g 0.264 2 37.91 0.7694
#>
#> Assumption checks
#>
#> normality
#> group n statistic p_value note
#> a 20 0.9533 0.4195 <NA>
#> b 20 0.9462 0.3127 <NA>
#> c 20 0.9691 0.7352 <NA>
#>
#> variance_ratio
#> 1.272
#>
#> Effect sizes
#> group1 group2 hedges_g conf_low conf_high magnitude standardiser
#> a b 0.21630 -0.4025 0.8410 small sqrt((s1^2 + s2^2) / 2)
#> a c 0.05873 -0.5604 0.6795 negligible sqrt((s1^2 + s2^2) / 2)
#> b c -0.16930 -0.7926 0.4494 negligible sqrt((s1^2 + s2^2) / 2)
#>
#> Group summaries (95% intervals)
#> group n mean sd se conf_low conf_high
#> a 20 0.190500 0.9133 0.2042 -0.2369 0.6179
#> b 20 -0.006472 0.8714 0.1948 -0.4143 0.4013
#> c 20 0.138800 0.8097 0.1811 -0.2402 0.5178
#>
#> Pairwise comparisons
#> group1 group2 difference conf_low conf_high statistic df p_value p_adjusted
#> a b 0.19700 -0.3744 0.7684 0.6979 37.92 0.4895 1
#> a c 0.05173 -0.5010 0.6045 0.1895 37.46 0.8507 1
#> b c -0.14530 -0.6838 0.3933 -0.5462 37.80 0.5882 1
#> adjustment
#> holm
#> holm
#> holm