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 class 'polr'
augment(
x,
data = model.frame(x),
newdata = NULL,
type.predict = c("class"),
...
)
```

## Arguments

- x
A

`polr`

object returned from`MASS::polr()`

.- 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
Which type of prediction to compute, passed to

`MASS:::predict.polr()`

. Only supports`"class"`

at the moment.- ...
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 ordinal tidiers:
`augment.clm()`

,
`glance.clm()`

,
`glance.clmm()`

,
`glance.polr()`

,
`glance.svyolr()`

,
`tidy.clm()`

,
`tidy.clmm()`

,
`tidy.polr()`

,
`tidy.svyolr()`

## Examples

```
# load libraries for models and data
library(MASS)
# fit model
fit <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
# summarize model fit with tidiers
tidy(fit, exponentiate = TRUE, conf.int = TRUE)
#>
#> Re-fitting to get Hessian
#> # A tibble: 8 × 7
#> term estimate std.error statistic conf.low conf.high coef.type
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 InflMedium 1.76 0.105 5.41 1.44 2.16 coefficie…
#> 2 InflHigh 3.63 0.127 10.1 2.83 4.66 coefficie…
#> 3 TypeApartment 0.564 0.119 -4.80 0.446 0.712 coefficie…
#> 4 TypeAtrium 0.693 0.155 -2.36 0.511 0.940 coefficie…
#> 5 TypeTerrace 0.336 0.151 -7.20 0.249 0.451 coefficie…
#> 6 ContHigh 1.43 0.0955 3.77 1.19 1.73 coefficie…
#> 7 Low|Medium 0.609 0.125 -3.97 NA NA scale
#> 8 Medium|High 2.00 0.125 5.50 NA NA scale
glance(fit)
#> # A tibble: 1 × 7
#> edf logLik AIC BIC deviance df.residual nobs
#> <int> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 8 -1740. 3495. 3539. 3479. 1673 1681
augment(fit, type.predict = "class")
#> # A tibble: 72 × 6
#> Sat Infl Type Cont `(weights)` .fitted
#> <ord> <fct> <fct> <fct> <int> <fct>
#> 1 Low Low Tower Low 21 Low
#> 2 Medium Low Tower Low 21 Low
#> 3 High Low Tower Low 28 Low
#> 4 Low Medium Tower Low 34 High
#> 5 Medium Medium Tower Low 22 High
#> 6 High Medium Tower Low 36 High
#> 7 Low High Tower Low 10 High
#> 8 Medium High Tower Low 11 High
#> 9 High High Tower Low 36 High
#> 10 Low Low Apartment Low 61 Low
#> # ℹ 62 more rows
fit2 <- polr(factor(gear) ~ am + mpg + qsec, data = mtcars)
tidy(fit, p.values = TRUE)
#>
#> Re-fitting to get Hessian
#> p-values can presently only be returned for models that contain
#> no categorical variables with more than two levels
#> # A tibble: 8 × 6
#> term estimate std.error statistic p.value coef.type
#> <chr> <dbl> <dbl> <dbl> <lgl> <chr>
#> 1 InflMedium 0.566 0.105 5.41 NA coefficient
#> 2 InflHigh 1.29 0.127 10.1 NA coefficient
#> 3 TypeApartment -0.572 0.119 -4.80 NA coefficient
#> 4 TypeAtrium -0.366 0.155 -2.36 NA coefficient
#> 5 TypeTerrace -1.09 0.151 -7.20 NA coefficient
#> 6 ContHigh 0.360 0.0955 3.77 NA coefficient
#> 7 Low|Medium -0.496 0.125 -3.97 NA scale
#> 8 Medium|High 0.691 0.125 5.50 NA scale
```