Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for pairwise.htest tidy(x, ...)
x | A |
---|---|
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
Note that in one-sided tests, the alternative hypothesis of each test can be stated as "group1 is greater/less than group2".
Note also that the columns of group1 and group2 will always be a factor, even if the original input is (e.g.) numeric.
stats::pairwise.t.test()
, stats::pairwise.wilcox.test()
,
tidy()
Other htest tidiers:
augment.htest()
,
tidy.htest()
,
tidy.power.htest()
A tibble::tibble()
with columns:
First group being compared.
Second group being compared.
The two-sided p-value associated with the observed statistic.
attach(airquality) Month <- factor(Month, labels = month.abb[5:9]) ptt <- pairwise.t.test(Ozone, Month) tidy(ptt) #> # A tibble: 10 × 3 #> group1 group2 p.value #> <chr> <chr> <dbl> #> 1 Jun May 1 #> 2 Jul May 0.000264 #> 3 Jul Jun 0.0511 #> 4 Aug May 0.000195 #> 5 Aug Jun 0.0499 #> 6 Aug Jul 1 #> 7 Sep May 1 #> 8 Sep Jun 1 #> 9 Sep Jul 0.00488 #> 10 Sep Aug 0.00388 library(modeldata) data(hpc_data) attach(hpc_data) ptt2 <- pairwise.t.test(compounds, class) tidy(ptt2) #> # A tibble: 6 × 3 #> group1 group2 p.value #> <chr> <chr> <dbl> #> 1 F VF 9.28e- 8 #> 2 M VF 2.55e- 61 #> 3 M F 4.26e- 34 #> 4 L VF 2.52e-126 #> 5 L F 5.44e- 95 #> 6 L M 2.45e- 25 tidy(pairwise.t.test(compounds, class, alternative = "greater")) #> # A tibble: 6 × 3 #> group1 group2 p.value #> <chr> <chr> <dbl> #> 1 F VF 4.64e- 8 #> 2 M VF 1.27e- 61 #> 3 M F 2.13e- 34 #> 4 L VF 1.26e-126 #> 5 L F 2.72e- 95 #> 6 L M 1.22e- 25 tidy(pairwise.t.test(compounds, class, alternative = "less")) #> # A tibble: 6 × 3 #> group1 group2 p.value #> <chr> <chr> <dbl> #> 1 F VF 1 #> 2 M VF 1 #> 3 M F 1 #> 4 L VF 1 #> 5 L F 1 #> 6 L M 1 tidy(pairwise.wilcox.test(compounds, class)) #> # A tibble: 6 × 3 #> group1 group2 p.value #> <chr> <chr> <dbl> #> 1 F VF 4.85e-32 #> 2 M VF 2.41e-66 #> 3 M F 1.45e-23 #> 4 L VF 1.90e-77 #> 5 L F 1.28e-42 #> 6 L M 6.84e- 9