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.
# S3 method for confint.glht
tidy(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
. 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::confint.glht()
, multcomp::glht()
Other multcomp tidiers:
tidy.cld()
,
tidy.glht()
,
tidy.summary.glht()
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
Levels being compared.
The estimated value of the regression term.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("multcomp", quietly = TRUE)) {
# 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)
}
#> # A tibble: 3 × 2
#> tension letters
#> <chr> <chr>
#> 1 L b
#> 2 M a
#> 3 H a