broom (version 1.0.4)

tidy.confint.glht: Tidy a(n) confint.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 confint.glht
tidy(x, ...)

Value

A tibble::tibble() with columns:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

contrast

Levels being compared.

estimate

The estimated value of the regression term.

Arguments

x

A confint.glht object created by calling multcomp::confint.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. 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::confint.glht(), multcomp::glht()

Other multcomp tidiers: tidy.cld(), tidy.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)
}