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 summary.glht
tidy(x, ...)

## Arguments

x A summary.glht object created by calling multcomp::summary.glht() on a glht object created with multcomp::glht(). 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.

tidy(), multcomp::summary.glht(), multcomp::glht()

Other multcomp tidiers: tidy.cld, tidy.confint.glht, tidy.glht

## Value

A tibble::tibble() with columns:

estimate

The estimated value of the regression term.

lhs

TODO

p.value

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

rhs

TODO

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

std.error

The standard error of the regression term.

## Examples


library(multcomp)
library(ggplot2)

amod <- aov(breaks ~ wool + tension, data = warpbreaks)
wht <- glht(amod, linfct = mcp(tension = "Tukey"))

tidy(wht)#> # A tibble: 3 x 3
#>   lhs     rhs estimate
#>   <chr> <dbl>    <dbl>
#> 1 M - L     0   -10.
#> 2 H - L     0   -14.7
#> 3 H - M     0    -4.72ggplot(wht, aes(lhs, estimate)) + geom_point()
CI <- confint(wht)
tidy(CI)#> # A tibble: 3 x 5
#>   lhs     rhs estimate conf.low conf.high
#>   <chr> <dbl>    <dbl>    <dbl>     <dbl>
#> 1 M - L     0   -10.      -19.4    -0.649
#> 2 H - L     0   -14.7     -24.1    -5.37
#> 3 H - M     0    -4.72    -14.1     4.63 ggplot(CI, aes(lhs, estimate, ymin = lwr, ymax = upr)) +
geom_pointrange()
tidy(summary(wht))#> # A tibble: 3 x 6
#>   lhs     rhs estimate std.error statistic p.value
#>   <chr> <dbl>    <dbl>     <dbl>     <dbl>   <dbl>
#> 1 M - L     0   -10.        3.87     -2.58 0.0337
#> 2 H - L     0   -14.7       3.87     -3.80 0.00115
#> 3 H - M     0    -4.72      3.87     -1.22 0.447  ggplot(mapping = aes(lhs, estimate)) +
geom_linerange(aes(ymin = lwr, ymax = upr), data = CI) +
geom_point(aes(size = p), data = summary(wht)) +
scale_size(trans = "reverse")
cld <- cld(wht)
tidy(cld)#> # A tibble: 3 x 2
#>   lhs   letters
#>   <chr> <chr>
#> 1 L     b
#> 2 M     a
#> 3 H     a