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
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
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 all columns used to fit
the model are present.
Augment will often behavior different depending on whether
newdata is specified. 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
augment(fit) will return the augmented training data. In these
cases augment tries to reconstruct the original data based on the model
object, with some 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. At this time, tibbles do not
support matrix-columns. This means you should not specify a matrix
of covariates in a model formula during the original model fitting
process, and that
survival::Surv() objects are not supported in input data. If you
encounter errors, try explicitly passing a tibble, or fitting the original
model on data in a tibble.
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.
# S3 method for polr augment(x, data = model.frame(x), newdata = NULL, type.predict = c("class"), ...)
Which type of prediction to compute,
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
library(MASS) fit <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) tidy(fit, exponentiate = TRUE, conf.int = TRUE)#> #>#>#> #>#> # A tibble: 8 x 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 coefficient #> 2 InflHigh 3.63 0.127 10.1 2.83 4.66 coefficient #> 3 TypeApartment 0.564 0.119 -4.80 0.446 0.712 coefficient #> 4 TypeAtrium 0.693 0.155 -2.36 0.511 0.940 coefficient #> 5 TypeTerrace 0.336 0.151 -7.20 0.249 0.451 coefficient #> 6 ContHigh 1.43 0.0955 3.77 1.19 1.73 coefficient #> 7 Low|Medium 0.609 0.125 -3.97 NA NA scale #> 8 Medium|High 2.00 0.125 5.50 NA NA scaleglance(fit)#> # A tibble: 1 x 7 #> edf logLik AIC BIC deviance df.residual nobs #> <int> <dbl> <dbl> <dbl> <dbl> <int> <int> #> 1 8 -1740. 3495. 3539. 3479. 1673 1681augment(fit, type.predict = "class")#> # A tibble: 72 x 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 #> # … with 62 more rows