View source: R/multcomp-tidiers.R
tidy.glht | R Documentation |
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 class 'glht'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
x |
A |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level |
The confidence level to use for the confidence interval
if |
... |
Additional arguments passed to |
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. |
tidy()
, multcomp::glht()
Other multcomp tidiers:
tidy.cld()
,
tidy.confint.glht()
,
tidy.summary.glht()
# 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)
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