# Other predict methods.

Source:`R/predict_class.R`

, `R/predict_classprob.R`

, `R/predict_hazard.R`

, and 6 more
`other_predict.Rd`

These are internal functions not meant to be directly called by the user.

## Usage

```
# S3 method for model_fit
predict_class(object, new_data, ...)
# S3 method for model_fit
predict_classprob(object, new_data, ...)
# S3 method for model_fit
predict_hazard(object, new_data, eval_time, time = deprecated(), ...)
# S3 method for model_fit
predict_confint(object, new_data, level = 0.95, std_error = FALSE, ...)
predict_confint(object, ...)
predict_predint(object, ...)
# S3 method for model_fit
predict_predint(object, new_data, level = 0.95, std_error = FALSE, ...)
predict_predint(object, ...)
# S3 method for model_fit
predict_linear_pred(object, new_data, ...)
predict_linear_pred(object, ...)
# S3 method for model_fit
predict_numeric(object, new_data, ...)
predict_numeric(object, ...)
# S3 method for model_fit
predict_quantile(
object,
new_data,
quantile = (1:9)/10,
interval = "none",
level = 0.95,
...
)
# S3 method for model_fit
predict_survival(
object,
new_data,
eval_time,
time = deprecated(),
interval = "none",
level = 0.95,
...
)
predict_survival(object, ...)
# S3 method for model_fit
predict_time(object, new_data, ...)
predict_time(object, ...)
```

## Arguments

- object
An object of class

`model_fit`

.- new_data
A rectangular data object, such as a data frame.

- ...
Additional

`parsnip`

-related options, depending on the value of`type`

. Arguments to the underlying model's prediction function cannot be passed here (use the`opts`

argument instead). Possible arguments are:`interval`

: for`type`

equal to`"survival"`

or`"quantile"`

, should interval estimates be added, if available? Options are`"none"`

and`"confidence"`

.`level`

: for`type`

equal to`"conf_int"`

,`"pred_int"`

, or`"survival"`

, this is the parameter for the tail area of the intervals (e.g. confidence level for confidence intervals). Default value is`0.95`

.`std_error`

: for`type`

equal to`"conf_int"`

or`"pred_int"`

, add the standard error of fit or prediction (on the scale of the linear predictors). Default value is`FALSE`

.`quantile`

: for`type`

equal to`quantile`

, the quantiles of the distribution. Default is`(1:9)/10`

.`eval_time`

: for`type`

equal to`"survival"`

or`"hazard"`

, the time points at which the survival probability or hazard is estimated.

- level
A single numeric value between zero and one for the interval estimates.

- std_error
A single logical for whether the standard error should be returned (assuming that the model can compute it).

- quantile
A vector of numbers between 0 and 1 for the quantile being predicted.