broom (version 1.0.4)

tidy.glht: Tidy a(n) glht object

Description

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.

Usage

# S3 method for glht
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Value

A tibble::tibble() with columns:

contrast

Levels being compared.

estimate

The estimated value of the regression term.

null.value

Value to which the estimate is compared.

Arguments

x

A glht object returned by multcomp::glht().

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

...

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. Two exceptions here are:

• tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

• augment() methods will warn when supplied a newdata argument if it will be ignored.

See Also

tidy(), multcomp::glht()

Other multcomp tidiers: tidy.cld(), tidy.confint.glht(), tidy.summary.glht()

Examples

if (FALSE) { # rlang::is_installed(c("multcomp", "ggplot2"))

# load libraries for models and data
library(multcomp)
library(ggplot2)

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

tidy(wht)

ggplot(wht, aes(lhs, estimate)) +
  geom_point()

CI <- confint(wht)

tidy(CI)

ggplot(CI, aes(lhs, estimate, ymin = lwr, ymax = upr)) +
  geom_pointrange()

tidy(summary(wht))
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)
}