augment.rq {broom} | R Documentation

## Augment data with information from a(n) rq object

### Description

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 all columns used to fit the model are present.

Augment will often behavior different depending on whether `data` or `newdata` is specified. 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 some 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. At this time, tibbles do not support matrix-columns. This means you should not specify a matrix of covariates in a model formula during the original model fitting process, and that `splines::ns()`, `stats::poly()` and `survival::Surv()` objects are not supported in input data. If you encounter errors, try explicitly passing a tibble, or fitting the original model on data in a tibble.

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 `rq` object returned from `quantreg::rq()`.

`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.

`...`

Arguments passed on to `quantreg::predict.rq`

object

  object of class rq or rqs or rq.process produced by `rq`

newdata

  An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.

interval

  type 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.

level

  converage probability for the 'confidence' intervals.

type

  For `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. For `predict.rqs` and `predict.rq.process` when `stepfun = TRUE`, `type` is "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 function `rearrange`. When the "fhat" option is invoked, a list of conditional density functions is returned based on Silverman's adaptive kernel method as implemented in `akj` and `approxfun`.

na.action

  function determining what should be done with missing values in 'newdata'. The default is to predict 'NA'.

### Value

A tibble::tibble with one row per obseration and columns:

`.resid` | Residuals
`.fitted` | Fitted quantiles of the model
`.tau` | Quantile estimated

Depending on the arguments passed on to `predict.rq` via `...`, a confidence interval is also calculated on the fitted values resulting in columns:

`.conf.low` | Lower confidence interval value
`.conf.high` | Upper confidence interval value

`predict.rq` does not provide confidence intervals when `newdata` is provided.

### See Also

augment, `quantreg::rq()`, `quantreg::predict.rq()`

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

[Package broom version 0.5.2 Index]