Tidy a(n) nlrq object

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 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. Two exceptions here are:

• tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

• augment() methods will warn when supplied a newdata argument if it will be ignored.

See also

augment(), quantreg::nlrq()

Other quantreg tidiers: augment.rqs(), augment.rq(), glance.nlrq(), glance.rq(), tidy.nlrq(), tidy.rqs(), tidy.rq()

Examples

# 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 
#> # ℹ 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
#>   <chr>        <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
#> 2 Mazda RX4 W…  21       6   160   110  3.9   3.88  17.0     0     1     4
#> 3 Datsun 710    22.8     4   108    93  3.85  3.32  18.6     1     1     4
#> 4 Hornet 4 Dr…  21.4     6   258   110  3.08  4.22  19.4     1     0     3
#> 5 Hornet Spor…  18.7     8   360   175  3.15  4.44  17.0     0     0     3
#> 6 Valiant       18.1     6   225   105  2.76  4.46  20.2     1     0     3
#> # ℹ 2 more variables: carb <dbl>, .fitted <dbl>