Tidy a(n) pairwise.htest object

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.

Value

A tibble::tibble with one row per group/group comparison and columns:

group1

First group being compared

group2

Second group being compared

p.value

(Adjusted) p-value of comparison

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

stats::pairwise.t.test(), stats::pairwise.wilcox.test(), tidy()

Other htest tidiers: augment.htest, tidy.htest, tidy.power.htest

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