broom (version 0.8.0)

tidy.negbin: Tidy a(n) negbin 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 negbin
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

Arguments

x

A glm.nb object returned by MASS::glm.nb().

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

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.

exponentiate

Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.

...

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.

See Also

MASS::glm.nb()

Other glm.nb tidiers: glance.negbin()

Examples

# NOT RUN {
# feel free to ignore the following line<U+2014>it allows {broom} to supply 
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("MASS", quietly = TRUE)) {
 
# load libraries for models and data
library(MASS)

# fit model
r <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine)

# summarize model fit with tidiers
tidy(r)
glance(r)

}

# }