Augment accepts a model object and a dataset and adds
information about each observation in the dataset. Most commonly, this
includes predicted values in the `.fitted`

column, residuals in the
`.resid`

column, and standard errors for the fitted values in a `.se.fit`

column. New columns always begin with a `.`

prefix to avoid overwriting
columns in the original dataset.

Users may pass data to augment via either the `data`

argument or the
`newdata`

argument. If the user passes data to the `data`

argument,
it **must** be exactly the data that was used to fit the model
object. Pass datasets to `newdata`

to augment data that was not used
during model fitting. This still requires that at least all predictor
variable columns used to fit the model are present. If the original outcome
variable used to fit the model is not included in `newdata`

, then no
`.resid`

column will be included in the output.

Augment will often behave differently depending on whether `data`

or
`newdata`

is given. This is because there is often information
associated with training observations (such as influences or related)
measures that is not meaningfully defined for new observations.

For convenience, many augment methods provide default `data`

arguments,
so that `augment(fit)`

will return the augmented training data. In these
cases, augment tries to reconstruct the original data based on the model
object with varying degrees of success.

The augmented dataset is always returned as a tibble::tibble with the
**same number of rows** as the passed dataset. This means that the passed
data must be coercible to a tibble. If a predictor enters the model as part
of a matrix of covariates, such as when the model formula uses
`splines::ns()`

, `stats::poly()`

, or `survival::Surv()`

, it is represented
as a matrix column.

We are in the process of defining behaviors for models fit with various
`na.action`

arguments, but make no guarantees about behavior when data is
missing at this time.

## Usage

```
# S3 method for glm
augment(
x,
data = model.frame(x),
newdata = NULL,
type.predict = c("link", "response", "terms"),
type.residuals = c("deviance", "pearson"),
se_fit = FALSE,
...
)
```

## Arguments

- x
A

`glm`

object returned from`stats::glm()`

.- data
A base::data.frame or

`tibble::tibble()`

containing the original data that was used to produce the object`x`

. Defaults to`stats::model.frame(x)`

so that`augment(my_fit)`

returns the augmented original data.**Do not**pass new data to the`data`

argument. Augment will report information such as influence and cooks distance for data passed to the`data`

argument. These measures are only defined for the original training data.- newdata
A

`base::data.frame()`

or`tibble::tibble()`

containing all the original predictors used to create`x`

. Defaults to`NULL`

, indicating that nothing has been passed to`newdata`

. If`newdata`

is specified, the`data`

argument will be ignored.- type.predict
Passed to

`stats::predict.glm()`

`type`

argument. Defaults to`"link"`

.- type.residuals
Passed to

`stats::residuals.glm()`

and to`stats::rstandard.glm()`

`type`

arguments. Defaults to`"deviance"`

.- se_fit
Logical indicating whether or not a

`.se.fit`

column should be added to the augmented output. For some models, this calculation can be somewhat time-consuming. 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 the weights for any of the observations in the model are 0, then columns ".infl" and ".hat" in the result will be 0 for those observations.

A `.resid`

column is not calculated when data is specified via
the `newdata`

argument.

## See also

Other lm tidiers:
`augment.lm()`

,
`glance.glm()`

,
`glance.lm()`

,
`glance.summary.lm()`

,
`glance.svyglm()`

,
`tidy.glm()`

,
`tidy.lm.beta()`

,
`tidy.lm()`

,
`tidy.mlm()`

,
`tidy.summary.lm()`

## Value

A `tibble::tibble()`

with columns:

- .cooksd
Cooks distance.

- .fitted
Fitted or predicted value.

- .hat
Diagonal of the hat matrix.

- .resid
The difference between observed and fitted values.

- .se.fit
Standard errors of fitted values.

- .sigma
Estimated residual standard deviation when corresponding observation is dropped from model.

- .std.resid
Standardised residuals.