augment.nlrq {broom} | R Documentation |
Tidy a(n) nlrq object
Description
Tidy summarizes information about the components of a model.
A model component might be a single term in a regression, a single
hypothesis, a cluster, or a class. Exactly what tidy considers to be a
model component varies across models but is usually self-evident.
If a model has several distinct types of components, you will need to
specify which components to return.
Usage
## S3 method for class 'nlrq'
augment(x, data = NULL, newdata = NULL, ...)
Arguments
x |
A nlrq object returned from quantreg::nlrq() .
|
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 . 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.
|
See Also
augment()
, quantreg::nlrq()
Other quantreg tidiers:
augment.rqs()
,
augment.rq()
,
glance.nlrq()
,
glance.rq()
,
tidy.nlrq()
,
tidy.rqs()
,
tidy.rq()
Examples
n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2))
tidy(n)
augment(n)
glance(n)
library(ggplot2)
ggplot(augment(n), aes(wt, mpg)) +
geom_point() +
geom_line(aes(y = .fitted))
newdata <- head(mtcars)
newdata$wt <- newdata$wt + 1
augment(n, newdata = newdata)
[Package
broom version 0.7.7
Index]