Augment data with information from a flexsurv model object

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.

Usage

# S3 method for flexsurvreg
augment(
  x,
  data = NULL,
  newdata = NULL,
  type.predict = "response",
  type.residuals = "response",
  ...
)

Arguments

x: Output from flexsurvreg or flexsurvspline, representing a fitted survival model object.
data: A data.frame or tibble containing the original data that was used to produce the object x.
newdata: A data.frame or 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 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 residuals generic. Allowed arguments vary with model class, so be sure to read the residuals.my_class documentation.
...: Additional arguments. Not currently used.

Value

A tibble containing data or newdata and possible additional columns:

.fitted Fitted values of model
.se.fit Standard errors of fitted values
.resid Residuals (not present if newdata specified)

Details

If neither of data or newdata are specified, then model.frame(x) will be used. It is worth noting that model.frame(x) will include a Surv object and not the original time-to-event variables used when fitting the flexsurvreg object. If the original data is desired, specify data.

Examples

fit <- flexsurvreg(formula = Surv(futime, fustat) ~ age, data = ovarian, dist = "exp")
augment(fit, data = ovarian)
#> # A tibble: 26 × 9
#>    futime fustat   age resid.ds    rx ecog.ps .pred_time .std_error  .resid
#>     <dbl>  <dbl> <dbl>    <dbl> <dbl>   <dbl>      <dbl>      <dbl>   <dbl>
#>  1     59      1  72.3        2     1       1       213.       113.  -154. 
#>  2    115      1  74.5        2     1       1       165.       103.   -49.8
#>  3    156      1  66.5        2     1       2       427.       149.  -271. 
#>  4    421      0  53.4        2     2       1      2016.       879. -1595. 
#>  5    431      1  50.3        2     1       1      2886.      1576. -2455. 
#>  6    448      0  56.4        1     1       2      1402.       495.  -954. 
#>  7    464      1  56.9        2     2       2      1320.       452.  -856. 
#>  8    475      1  59.9        2     2       2       934.       281.  -459. 
#>  9    477      0  64.2        2     1       1       560.       174.   -82.8
#> 10    563      1  55.2        1     2       2      1626.       623. -1063. 
#> # ℹ 16 more rows