R: Augment data with information from a(n) survreg object

augment.survreg {broom}

R Documentation

Augment data with information from a(n) survreg 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 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 'survreg'
augment(
  x,
  data = model.frame(x),
  newdata = NULL,
  type.predict = "response",
  type.residuals = "response",
  ...
)

Arguments

`x`	An `survreg` object returned from `survival::survreg()`.
`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.
`type.predict`	Character indicating type of prediction to use. Passed to the `type` argument of the `stats::predict()` generic. Allowed arguments vary with model class, so be sure to read the `predict.my_class` documentation.
`type.residuals`	Character indicating type of residuals to use. Passed to the `type` argument of `stats::residuals()` generic. Allowed arguments vary with model class, so be sure to read the `residuals.my_class` documentation.
`...`	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.

Value

A tibble::tibble() with columns:

`.fitted`	Fitted or predicted value.
`.resid`	The difference between observed and fitted values.
`.se.fit`	Standard errors of fitted values.

Examples



# load libraries for models and data
library(survival)

# fit model
sr <- survreg(
  Surv(futime, fustat) ~ ecog.ps + rx,
  ovarian,
  dist = "exponential"
)

# summarize model fit with tidiers + visualization
tidy(sr)
augment(sr, ovarian)
glance(sr)

# coefficient plot
td <- tidy(sr, conf.int = TRUE)

library(ggplot2)

ggplot(td, aes(estimate, term)) +
  geom_point() +
  geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0) +
  geom_vline(xintercept = 0)