View source: R/quantregrqstidiers.R
augment.rqs  R Documentation 
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. At this time, tibbles do not
support matrixcolumns. 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 class 'rqs' augment(x, data = model.frame(x), newdata, ...)
x 
An 
data 
A base::data.frame or 
newdata 
A 
... 
Arguments passed on to

Depending on the arguments passed on to predict.rq
via ...
,
a confidence interval is also calculated on the fitted values resulting in
columns .lower
and .upper
. Does not provide confidence
intervals when data is specified via the newdata
argument.
augment, quantreg::rq()
, quantreg::predict.rqs()
Other quantreg tidiers:
augment.nlrq()
,
augment.rq()
,
glance.nlrq()
,
glance.rq()
,
tidy.nlrq()
,
tidy.rqs()
,
tidy.rq()
# load modeling library and data library(quantreg) data(stackloss) # median (l1) regression fit for the stackloss data. mod1 < rq(stack.loss ~ stack.x, .5) # weighted sample median mod2 < rq(rnorm(50) ~ 1, weights = runif(50)) # summarize model fit with tidiers tidy(mod1) glance(mod1) augment(mod1) tidy(mod2) glance(mod2) augment(mod2) # varying tau to generate an rqs object mod3 < rq(stack.loss ~ stack.x, tau = c(.25, .5)) tidy(mod3) augment(mod3) # glance cannot handle rqs objects like `mod3`use a purrr # `map`based workflow instead
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