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 betareg
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
A betareg
object produced by a call to betareg::betareg()
.
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to FALSE
.
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
. 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.
A tibble::tibble()
with one row for each term in the
regression. The tibble has columns:
The name of the regression term.
The estimated value of the regression term.
The standard error of the regression term.
The value of a statistic, almost always a T-statistic, to use in a hypothesis that the regression term is non-zero.
The two-sided p-value associated with the observed statistic.
The low end of a confidence interval for the regression
term. Included only if conf.int = TRUE
.
The high end of a confidence interval for the regression
term. Included only if conf.int = TRUE
.
In additional the standard columns, the returned tibble has an additional column component. component indicates whether a particular term was used to model either the "mean" or "precision". Here the precision is the inverse of the variance, often referred to as phi. At least one term will have been used to model phi.
# NOT RUN {
library(betareg)
data("GasolineYield", package = "betareg")
mod <- betareg(yield ~ batch + temp, data = GasolineYield)
mod
tidy(mod)
tidy(mod, conf.int = TRUE)
tidy(mod, conf.int = TRUE, conf.level = .99)
augment(mod)
glance(mod)
# }
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