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 glm
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)
```

## Arguments

- x
A

`glm`

object returned from`stats::glm()`

.- 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:

## See also

Other lm tidiers:
`augment.glm()`

,
`augment.lm()`

,
`glance.glm()`

,
`glance.lm()`

,
`glance.summary.lm()`

,
`glance.svyglm()`

,
`tidy.lm.beta()`

,
`tidy.lm()`

,
`tidy.mlm()`

,
`tidy.summary.lm()`