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 gam
augment(
x,
data = model.frame(x),
newdata = NULL,
type.predict,
type.residuals,
...
)
```

## Arguments

- x
A

`gam`

object returned from a call to`mgcv::gam()`

.- 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
Character indicating type of prediction to use. Passed to the

`type`

argument of the`stats::predict()`

generic. Allowed arguments vary with model class, so be sure to read the`predict.my_class`

documentation.- type.residuals
Character indicating type of residuals to use. Passed to the

`type`

argument of`stats::residuals()`

generic. Allowed arguments vary with model class, so be sure to read the`residuals.my_class`

documentation.- ...
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

For additional details on Cook's distance, see
`stats::cooks.distance()`

.

## 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.

## Examples

```
# load libraries for models and data
library(mgcv)
#> Loading required package: nlme
#>
#> Attaching package: ‘nlme’
#> The following object is masked from ‘package:dplyr’:
#>
#> collapse
#> This is mgcv 1.9-1. For overview type 'help("mgcv-package")'.
#>
#> Attaching package: ‘mgcv’
#> The following object is masked from ‘package:mclust’:
#>
#> mvn
# fit model
g <- gam(mpg ~ s(hp) + am + qsec, data = mtcars)
# summarize model fit with tidiers
tidy(g)
#> # A tibble: 1 × 5
#> term edf ref.df statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 s(hp) 2.36 3.02 6.34 0.00218
tidy(g, parametric = TRUE)
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 16.7 9.83 1.70 0.101
#> 2 am 4.37 1.56 2.81 0.00918
#> 3 qsec 0.0904 0.525 0.172 0.865
glance(g)
#> # A tibble: 1 × 9
#> df logLik AIC BIC deviance df.residual nobs adj.r.squared npar
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
#> 1 5.36 -74.4 162. 171. 196. 26.6 32 0.797 12
augment(g)
#> # A tibble: 32 × 11
#> .rownames mpg am qsec hp .fitted .se.fit .resid .hat .sigma
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 Mazda RX4 21 1 16.5 110 24.3 1.03 -3.25 0.145 NA
#> 2 Mazda RX4… 21 1 17.0 110 24.3 0.925 -3.30 0.116 NA
#> 3 Datsun 710 22.8 1 18.6 93 26.0 0.894 -3.22 0.109 NA
#> 4 Hornet 4 … 21.4 0 19.4 110 20.2 0.827 1.25 0.0930 NA
#> 5 Hornet Sp… 18.7 0 17.0 175 15.7 0.815 3.02 0.0902 NA
#> 6 Valiant 18.1 0 20.2 105 20.7 0.914 -2.56 0.113 NA
#> 7 Duster 360 14.3 0 15.8 245 12.7 1.11 1.63 0.167 NA
#> 8 Merc 240D 24.4 0 20 62 25.0 1.45 -0.618 0.287 NA
#> 9 Merc 230 22.8 0 22.9 95 21.8 1.81 0.959 0.446 NA
#> 10 Merc 280 19.2 0 18.3 123 19.0 0.864 0.211 0.102 NA
#> # ℹ 22 more rows
#> # ℹ 1 more variable: .cooksd <dbl>
```