Tidy a(n) TukeyHSD 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 TukeyHSD
tidy(x, ...)

Arguments

x

A TukeyHSD object return from stats::TukeyHSD().

...

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 comparison and columns:

term

Term for which levels are being compared

comparison

Levels being compared, separated by -

estimate

Estimate of difference

conf.low

Low end of confidence interval of difference

conf.high

High end of confidence interval of difference

adj.p.value

P-value adjusted for multiple comparisons

See also

tidy(), stats::TukeyHSD()

Other anova tidiers: tidy.anova, tidy.aovlist, tidy.aov, tidy.manova

Examples

fm1 <- aov(breaks ~ wool + tension, data = warpbreaks)
thsd <- TukeyHSD(fm1, "tension", ordered = TRUE)
tidy(thsd)
#> # A tibble: 3 x 6
#>   term    comparison estimate conf.low conf.high adj.p.value
#>   <chr>   <chr>         <dbl>    <dbl>     <dbl>       <dbl>
#> 1 tension M-H            4.72   -4.63       14.1     0.447  
#> 2 tension L-H           14.7     5.37       24.1     0.00112
#> 3 tension L-M           10.      0.647      19.4     0.0336 

# may include comparisons on multiple terms
fm2 <- aov(mpg ~ as.factor(gear) * as.factor(cyl), data = mtcars)
tidy(TukeyHSD(fm2))
#> # A tibble: 42 x 6
#>    term                       comparison estimate conf.low conf.high adj.p.value
#>    <chr>                      <chr>         <dbl>    <dbl>     <dbl>       <dbl>
#>  1 as.factor(gear)            4-3           8.43     5.19      11.7   0.00000297
#>  2 as.factor(gear)            5-3           5.27     0.955      9.59  0.0147    
#>  3 as.factor(gear)            5-4          -3.15    -7.60       1.30  0.201     
#>  4 as.factor(cyl)             6-4          -5.40    -9.45      -1.36  0.00748   
#>  5 as.factor(cyl)             8-4          -5.23    -8.60      -1.86  0.00201   
#>  6 as.factor(cyl)             8-6           0.172   -3.70       4.04  0.993     
#>  7 as.factor(gear):as.factor… 4:4-3:4       5.42    -6.65      17.5   0.832     
#>  8 as.factor(gear):as.factor… 5:4-3:4       6.70    -7.24      20.6   0.778     
#>  9 as.factor(gear):as.factor… 3:6-3:4      -1.75   -15.7       12.2   1.00      
#> 10 as.factor(gear):as.factor… 4:6-3:4      -1.75   -14.5       11.0   1.00      
#> # … with 32 more rows