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

- x
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`

. Two exceptions here are:

## 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.

## Value

A `tibble::tibble()`

with columns:

- conf.high
Upper bound on the confidence interval for the estimate.

- conf.low
Lower bound on the confidence interval for the estimate.

- estimate
The estimated value of the regression term.

- p.value
The two-sided p-value associated with the observed statistic.

- statistic
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

- std.error
The standard error of the regression term.

- term
The name of the regression term.

## Examples

```
# load modeling library
library(geepack)
# load data
data(state)
ds <- data.frame(state.region, state.x77)
# fit model
geefit <- geeglm(Income ~ Frost + Murder,
id = state.region,
data = ds,
corstr = "exchangeable"
)
# summarize model fit with tidiers
tidy(geefit)
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 4406. 407. 117. 0
#> 2 Frost 1.69 2.25 0.562 0.453
#> 3 Murder -22.7 31.4 0.522 0.470
tidy(geefit, conf.int = TRUE)
#> # A tibble: 3 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 4406. 407. 117. 0 3608. 5205.
#> 2 Frost 1.69 2.25 0.562 0.453 -2.72 6.10
#> 3 Murder -22.7 31.4 0.522 0.470 -84.2 38.8
```