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

Arguments

A geeglm object returned from a call to geepack::geeglm().

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.

Value

A tibble::tibble() with columns:

regresion

TRUE

Details

If conf.int = TRUE, the confidence interval is computed with the an internal confint.geeglm() function.

If you have missing values in your model data, you may need to refit the model with na.action = na.exclude or deal with the missingness in the data beforehand.

Examples

Run this code

# NOT RUN {
library(geepack)
data(state)

ds <- data.frame(state.region, state.x77)

geefit <- geeglm(Income ~ Frost + Murder,
  id = state.region,
  data = ds, family = gaussian,
  corstr = "exchangeable"
)

tidy(geefit)
tidy(geefit, conf.int = TRUE)
# }