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 cross 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, ...)

Arguments

x

A pairwise.htest object such as those returned from stats::pairwise.t.test() or stats::pairwise.wilcox.test().

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Details

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.

See also

Value

A tibble::tibble() with columns:

group1

First group being compared

group2

Second group being compared

p.value

The two-sided p-value associated with the observed statistic.

Examples

attach(airquality) Month <- factor(Month, labels = month.abb[5:9]) ptt <- pairwise.t.test(Ozone, Month) tidy(ptt)
#> # A tibble: 10 x 3 #> group1 group2 p.value #> * <chr> <chr> <dbl> #> 1 Jun May 1 #> 2 Jul May 0.000264 #> 3 Aug May 0.000195 #> 4 Sep May 1 #> 5 Jul Jun 0.0511 #> 6 Aug Jun 0.0499 #> 7 Sep Jun 1 #> 8 Aug Jul 1 #> 9 Sep Jul 0.00488 #> 10 Sep Aug 0.00388
attach(iris) ptt2 <- pairwise.t.test(Petal.Length, Species) tidy(ptt2)
#> # A tibble: 3 x 3 #> group1 group2 p.value #> * <chr> <chr> <dbl> #> 1 versicolor setosa 1.05e-68 #> 2 virginica setosa 1.23e-90 #> 3 virginica versicolor 1.81e-31
tidy(pairwise.t.test(Petal.Length, Species, alternative = "greater"))
#> # A tibble: 3 x 3 #> group1 group2 p.value #> * <chr> <chr> <dbl> #> 1 versicolor setosa 5.25e-69 #> 2 virginica setosa 6.16e-91 #> 3 virginica versicolor 9.05e-32
tidy(pairwise.t.test(Petal.Length, Species, alternative = "less"))
#> # A tibble: 3 x 3 #> group1 group2 p.value #> * <chr> <chr> <dbl> #> 1 versicolor setosa 1 #> 2 virginica setosa 1 #> 3 virginica versicolor 1
tidy(pairwise.wilcox.test(Petal.Length, Species))
#> # A tibble: 3 x 3 #> group1 group2 p.value #> * <chr> <chr> <dbl> #> 1 versicolor setosa 1.70e-17 #> 2 virginica setosa 1.70e-17 #> 3 virginica versicolor 9.13e-17