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. If a predictor enters the model as part
of a matrix of covariates, such as when the model formula uses
splines::ns(), stats::poly(), or survival::Surv(), it is represented
as a matrix column.
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.
Usage
# S3 method for class 'rq'
augment(x, data = model.frame(x), newdata = NULL, ...)Arguments
- x
An
rqobject returned fromquantreg::rq().- data
A base::data.frame or
tibble::tibble()containing the original data that was used to produce the objectx. Defaults tostats::model.frame(x)so thataugment(my_fit)returns the augmented original data. Do not pass new data to thedataargument. Augment will report information such as influence and cooks distance for data passed to thedataargument. These measures are only defined for the original training data.- newdata
A
base::data.frame()ortibble::tibble()containing all the original predictors used to createx. Defaults toNULL, indicating that nothing has been passed tonewdata. Ifnewdatais specified, thedataargument will be ignored.- ...
Arguments passed on to
quantreg::predict.rqobjectobject of class rq or rqs or rq.process produced by
rqintervaltype of interval desired: default is 'none', when set to 'confidence' the function returns a matrix predictions with point predictions for each of the 'newdata' points as well as lower and upper confidence limits.
levelconverage probability for the 'confidence' intervals.
typeFor
predict.rq, the method for 'confidence' intervals, if desired. If 'percentile' then one of the bootstrap methods is used to generate percentile intervals for each prediction, if 'direct' then a version of the Portnoy and Zhou (1998) method is used, and otherwise an estimated covariance matrix for the parameter estimates is used. Further arguments to determine the choice of bootstrap method or covariance matrix estimate can be passed via the ... argument. Forpredict.rqsandpredict.rq.processwhenstepfun = TRUE,typeis "Qhat", "Fhat" or "fhat" depending on whether the user would like to have estimates of the conditional quantile, distribution or density functions respectively. As noted below the two former estimates can be monotonized with the functionrearrange. When the "fhat" option is invoked, a list of conditional density functions is returned based on Silverman's adaptive kernel method as implemented inakjandapproxfun.na.actionfunction determining what should be done with missing values in 'newdata'. The default is to predict 'NA'.
Details
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.
See also
augment, quantreg::rq(), quantreg::predict.rq()
Other quantreg tidiers:
augment.nlrq(),
augment.rqs(),
glance.nlrq(),
glance.rq(),
tidy.nlrq(),
tidy.rq(),
tidy.rqs()
Value
A tibble::tibble() with columns:
- .fitted
Fitted or predicted value.
- .resid
The difference between observed and fitted values.
- .tau
Quantile.
Examples
# load modeling library and data
library(quantreg)
#> Loading required package: SparseM
#>
#> Attaching package: ‘SparseM’
#> The following object is masked from ‘package:Matrix’:
#>
#> det
#>
#> Attaching package: ‘quantreg’
#> The following object is masked from ‘package:survival’:
#>
#> untangle.specials
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)
#> # A tibble: 4 × 5
#> term estimate conf.low conf.high tau
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -39.7 -53.8 -24.5 0.5
#> 2 stack.xAir.Flow 0.832 0.509 1.17 0.5
#> 3 stack.xWater.Temp 0.574 0.272 3.04 0.5
#> 4 stack.xAcid.Conc. -0.0609 -0.278 0.0153 0.5
glance(mod1)
#> # A tibble: 1 × 5
#> tau logLik AIC BIC df.residual
#> <dbl> <logLik> <dbl> <dbl> <int>
#> 1 0.5 -50.15272 108. 112. 17
augment(mod1)
#> # A tibble: 21 × 5
#> stack.loss stack.x[,"Air.Flow"] [,"Water.Temp"] .resid .fitted .tau
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 42 80 27 5.06e+ 0 36.9 0.5
#> 2 37 80 27 -1.42e-14 37 0.5
#> 3 37 75 25 5.43e+ 0 31.6 0.5
#> 4 28 62 24 7.63e+ 0 20.4 0.5
#> 5 18 62 22 -1.22e+ 0 19.2 0.5
#> 6 18 62 23 -1.79e+ 0 19.8 0.5
#> 7 19 62 24 -1.00e+ 0 20 0.5
#> 8 20 62 24 -7.11e-15 20 0.5
#> 9 15 58 23 -1.46e+ 0 16.5 0.5
#> 10 14 58 18 -2.03e- 2 14.0 0.5
#> # ℹ 11 more rows
#> # ℹ 1 more variable: stack.x[3] <dbl>
tidy(mod2)
#> # A tibble: 1 × 5
#> term estimate conf.low conf.high tau
#> <chr> <dbl> <lgl> <lgl> <dbl>
#> 1 (Intercept) 0.124 NA NA 0.5
glance(mod2)
#> # A tibble: 1 × 5
#> tau logLik AIC BIC df.residual
#> <dbl> <logLik> <dbl> <dbl> <int>
#> 1 0.5 -78.76986 160. 161. 49
augment(mod2)
#> # A tibble: 50 × 5
#> `rnorm(50)` `(weights)` .resid .fitted .tau
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.393 0.696 0.269 0.124 0.5
#> 2 0.458 0.266 0.334 0.124 0.5
#> 3 -1.22 0.660 -1.34 0.124 0.5
#> 4 -1.12 0.212 -1.25 0.124 0.5
#> 5 0.993 0.00527 0.869 0.124 0.5
#> 6 -1.83 0.103 -1.96 0.124 0.5
#> 7 0.124 0.287 0 0.124 0.5
#> 8 0.591 0.444 0.467 0.124 0.5
#> 9 0.805 0.693 0.681 0.124 0.5
#> 10 0.00754 0.0209 -0.116 0.124 0.5
#> # ℹ 40 more rows
# varying tau to generate an rqs object
mod3 <- rq(stack.loss ~ stack.x, tau = c(.25, .5))
tidy(mod3)
#> # A tibble: 8 × 5
#> term estimate conf.low conf.high tau
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -3.6 e+ 1 -59.0 -7.84 0.25
#> 2 stack.xAir.Flow 5.00e- 1 0.229 0.970 0.25
#> 3 stack.xWater.Temp 1.00e+ 0 0.286 2.26 0.25
#> 4 stack.xAcid.Conc. -4.58e-16 -0.643 0.0861 0.25
#> 5 (Intercept) -3.97e+ 1 -53.8 -24.5 0.5
#> 6 stack.xAir.Flow 8.32e- 1 0.509 1.17 0.5
#> 7 stack.xWater.Temp 5.74e- 1 0.272 3.04 0.5
#> 8 stack.xAcid.Conc. -6.09e- 2 -0.278 0.0153 0.5
augment(mod3)
#> # A tibble: 42 × 5
#> stack.loss stack.x[,"Air.Flow"] [,"Water.Temp"] .tau .resid .fitted
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 42 80 27 0.25 1.10e+ 1 31.0
#> 2 42 80 27 0.5 5.06e+ 0 36.9
#> 3 37 80 27 0.25 6.00e+ 0 31.0
#> 4 37 80 27 0.5 -1.42e-14 37
#> 5 37 75 25 0.25 1.05e+ 1 26.5
#> 6 37 75 25 0.5 5.43e+ 0 31.6
#> 7 28 62 24 0.25 9.00e+ 0 19
#> 8 28 62 24 0.5 7.63e+ 0 20.4
#> 9 18 62 22 0.25 1.00e+ 0 17.0
#> 10 18 62 22 0.5 -1.22e+ 0 19.2
#> # ℹ 32 more rows
#> # ℹ 1 more variable: stack.x[3] <dbl>
# glance cannot handle rqs objects like `mod3`--use a purrr
# `map`-based workflow instead