# Augment data with information from a(n) speedlm object

Source:`R/speedglm-speedlm-tidiers.R`

`augment.speedlm.Rd`

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 speedlm
augment(x, data = model.frame(x), newdata = NULL, ...)
```

## Arguments

- x
A

`speedlm`

object returned from`speedglm::speedlm()`

.- 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.- ...
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 speedlm tidiers:
`glance.speedglm()`

,
`glance.speedlm()`

,
`tidy.speedglm()`

,
`tidy.speedlm()`

## Value

A `tibble::tibble()`

with columns:

- .fitted
Fitted or predicted value.

- .resid
The difference between observed and fitted values.

## Examples

```
# load modeling library
library(speedglm)
#> Loading required package: biglm
#> Loading required package: DBI
# fit model
mod <- speedlm(mpg ~ wt + qsec, data = mtcars, fitted = TRUE)
# summarize model fit with tidiers
tidy(mod)
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 19.7 5.25 3.76 7.65e- 4
#> 2 wt -5.05 0.484 -10.4 2.52e-11
#> 3 qsec 0.929 0.265 3.51 1.50e- 3
glance(mod)
#> # A tibble: 1 × 11
#> r.squared adj.r.squared statistic p.value df logLik AIC BIC
#> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 0.826 0.814 69.0 9.39e-12 3 -74.4 157. 163.
#> # ℹ 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>
augment(mod)
#> # A tibble: 32 × 6
#> .rownames mpg wt qsec .fitted .resid
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 21 2.62 16.5 21.8 -0.815
#> 2 Mazda RX4 Wag 21 2.88 17.0 21.0 -0.0482
#> 3 Datsun 710 22.8 2.32 18.6 25.3 -2.53
#> 4 Hornet 4 Drive 21.4 3.22 19.4 21.6 -0.181
#> 5 Hornet Sportabout 18.7 3.44 17.0 18.2 0.504
#> 6 Valiant 18.1 3.46 20.2 21.1 -2.97
#> 7 Duster 360 14.3 3.57 15.8 16.4 -2.14
#> 8 Merc 240D 24.4 3.19 20 22.2 2.17
#> 9 Merc 230 22.8 3.15 22.9 25.1 -2.32
#> 10 Merc 280 19.2 3.44 18.3 19.4 -0.185
#> # ℹ 22 more rows
```