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 cross 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, ...)

Arguments

x

A nlrq object returned from quantreg::nlrq().

data

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.

newdata

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.

...

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

Examples

n <- nls(mpg ~ k * e ^ wt, data = mtcars, start = list(k = 1, e = 2)) tidy(n)
#> # A tibble: 2 x 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
#> # A tibble: 32 x 3 #> mpg wt .fitted #> <dbl> <dbl> <dbl> #> 1 21 2.62 23.0 #> 2 21 2.88 21.4 #> 3 22.8 2.32 25.1 #> 4 21.4 3.22 19.3 #> 5 18.7 3.44 18.1 #> 6 18.1 3.46 18.0 #> 7 14.3 3.57 17.4 #> 8 24.4 3.19 19.5 #> 9 22.8 3.15 19.7 #> 10 19.2 3.44 18.1 #> # … with 22 more rows
#> # A tibble: 1 x 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 x 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 RX… 21 6 160 110 3.9 3.88 17.0 0 1 4 4 #> 3 Datsun 7… 22.8 4 108 93 3.85 3.32 18.6 1 1 4 1 #> 4 Hornet 4… 21.4 6 258 110 3.08 4.22 19.4 1 0 3 1 #> 5 Hornet S… 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>