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.
# S3 method for nlrq
augment(x, data = NULL, newdata = NULL, ...)
A nlrq
object returned from quantreg::nlrq()
.
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.
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.
Other quantreg tidiers:
augment.rqs()
,
augment.rq()
,
glance.nlrq()
,
glance.rq()
,
tidy.nlrq()
,
tidy.rqs()
,
tidy.rq()
# fit model
n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2))
# summarize model fit with tidiers + visualization
tidy(n)
#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 k 49.7 3.79 13.1 5.96e-14
#> 2 e 0.746 0.0199 37.5 8.86e-27
augment(n)
#> # A tibble: 32 × 4
#> mpg wt .fitted .resid
#> <dbl> <dbl> <dbl> <dbl>
#> 1 21 2.62 23.0 -2.01
#> 2 21 2.88 21.4 -0.352
#> 3 22.8 2.32 25.1 -2.33
#> 4 21.4 3.22 19.3 2.08
#> 5 18.7 3.44 18.1 0.611
#> 6 18.1 3.46 18.0 0.117
#> 7 14.3 3.57 17.4 -3.11
#> 8 24.4 3.19 19.5 4.93
#> 9 22.8 3.15 19.7 3.10
#> 10 19.2 3.44 18.1 1.11
#> # … with 22 more rows
glance(n)
#> # A tibble: 1 × 9
#> sigma isConv finTol logLik AIC BIC deviance df.residual nobs
#> <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 2.67 TRUE 0.00000204 -75.8 158. 162. 214. 30 32
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)
#> # A tibble: 6 × 13
#> .rownames mpg cyl disp hp drat wt qsec vs am gear carb
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 21 6 160 110 3.9 3.62 16.5 0 1 4 4
#> 2 Mazda RX4 W… 21 6 160 110 3.9 3.88 17.0 0 1 4 4
#> 3 Datsun 710 22.8 4 108 93 3.85 3.32 18.6 1 1 4 1
#> 4 Hornet 4 Dr… 21.4 6 258 110 3.08 4.22 19.4 1 0 3 1
#> 5 Hornet Spor… 18.7 8 360 175 3.15 4.44 17.0 0 0 3 2
#> 6 Valiant 18.1 6 225 105 2.76 4.46 20.2 1 0 3 1
#> # … with 1 more variable: .fitted <dbl>