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 all columns used to fit
the model are present.
Augment will often behavior different depending on whether data or
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 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 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 splines::ns(), stats::poly() and
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 lm
augment(
x,
data = stats::model.frame(x),
newdata,
type.predict,
type.residuals,
...
)An lm object created by stats::lm().
A 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.
A 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 of predictions to use when x is a glm object.
Passed to stats::predict.glm().
Type of residuals to use when x is a glm object.
Passed to stats::residuals.glm().
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. Additionally, if you pass
newdata = my_tibble to an augment() method that does not
accept a newdata argument, it will use the default value for
the data argument.
When newdata is not supplied augment.lm returns
one row for each observation, with seven columns added to the original
data:
Diagonal of the hat matrix
Estimate of residual standard deviation when corresponding observation is dropped from model
Cooks distance, cooks.distance()
Fitted values of model
Standard errors of fitted values
Residuals
Standardised residuals
Some unusual lm objects, such as rlm from MASS, may omit .cooksd and .std.resid. gam from mgcv omits .sigma.
When newdata is supplied, returns one row for each observation, with three columns added to the new data:
Fitted values of model
Standard errors of fitted values
Residuals of fitted values on the new data
When the modeling was performed with na.action = "na.omit"
(as is the typical default), rows with NA in the initial data are omitted
entirely from the augmented data frame. When the modeling was performed
with na.action = "na.exclude", one should provide the original data
as a second argument, at which point the augmented data will contain those
rows (typically with NAs in place of the new columns). If the original data
is not provided to augment() and na.action = "na.exclude", a
warning is raised and the incomplete rows are dropped.
augment(), stats::predict.lm()
Other lm tidiers:
augment.glm(),
glance.glm(),
glance.lm(),
tidy.glm(),
tidy.lm()