Nonparametric Inference

One Sample Wilcoxon test

one_wilcoxon_inference(wt ~ 1, data = mtcars2)

response	n	median	pseudomedian	conf.low	conf.high	null	V	p.value
wt	32	3.33	3.19	2.83	3.51	0.000	528	< 0.0001
Wilcoxon signed rank exact test (two.sided), with 95% confidence intervals.

Separately by another categorical variable

one_wilcoxon_inference(wt ~ am, data = mtcars2)

response	variable	n	median	pseudomedian	conf.low	conf.high	null	V	p.value
wt	am = automatic	19	3.52	3.63	3.44	4.27	0.000	190	< 0.0001
wt	am = manual	13	2.32	2.39	2.02	2.78	0.000	91.0	0.0002
Wilcoxon signed rank exact test (two.sided), with 95% confidence intervals.

Two Sample Wilcoxon test

two_wilcoxon_inference(wt ~ am, data = mtcars2)

response	variable	pseudomedian	conf.low	conf.high	null	W	p.value
wt	am: automatic - manual	1.28	0.80	1.79	0.000	230	< 0.0001
Wilcoxon rank sum exact test (two.sided), with 95% confidence intervals.

Kruskal-Wallis test

kruskal_wallis_test(wt ~ am, data=mtcars2)

response	variable	df	chisq	p.value
wt	am	1	16.9	< 0.0001
Kruskal-Wallis rank sum test

Pairwise Wilcoxon tests

pairwise_wilcoxon_inference(wt ~ cyl, data = mtcars2)

response	variable	pseudomedian	conf.low	conf.high	null	W	p.value	p.adjust
wt	cyl: 4 - 6	−0.94	−1.50	−0.25	0.000	8.00	0.0040	0.012
wt	cyl: 4 - 8	−1.62	−2.24	−0.98	0.000	1.00	< 0.0001	< 0.0001
wt	cyl: 6 - 8	−0.66	−1.88	−0.13	0.000	9.00	0.0015	0.0045
Wilcoxon rank sum exact test (two.sided), with 95% confidence intervals, adjusted for 3 comparisons using the Bonferroni method.
p-values adjusted for 3 multiple comparisons using the Bonferroni method.

Paired Wilcoxon test

paired_wilcoxon_inference(score2 - score1 ~ 1, data = passfail)

response	pseudomedian	conf.low	conf.high	null	V	p.value
score2 - score1	3.1	0.5	5.5	0.000	881	0.019
Wilcoxon signed rank test with continuity correction (two.sided), with 95% confidence intervals.