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 betareg 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. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
The tibble has one row for each term in the regression. The
component
column 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 the precision phi
.
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
Whether a particular term was used to model the mean or the precision in the regression. See details.
if (requireNamespace("betareg", quietly = TRUE)) { 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) } #> # A tibble: 1 × 7 #> pseudo.r.squared df.null logLik AIC BIC df.residual nobs #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> #> 1 0.962 30 84.8 -146. -128. 20 32