other_predict: Other predict methods.

In parsnip: A Common API to Modeling and Analysis Functions

Other predict methods.

Description

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

Usage

## S3 method for class 'model_fit'
predict_class(object, new_data, ...)

## S3 method for class 'model_fit'
predict_classprob(object, new_data, ...)

## S3 method for class 'model_fit'
predict_hazard(object, new_data, eval_time, time = deprecated(), ...)

## S3 method for class 'model_fit'
predict_confint(object, new_data, level = 0.95, std_error = FALSE, ...)

predict_confint(object, ...)

predict_predint(object, ...)

## S3 method for class 'model_fit'
predict_predint(object, new_data, level = 0.95, std_error = FALSE, ...)

predict_predint(object, ...)

## S3 method for class 'model_fit'
predict_linear_pred(object, new_data, ...)

predict_linear_pred(object, ...)

## S3 method for class 'model_fit'
predict_numeric(object, new_data, ...)

predict_numeric(object, ...)

## S3 method for class 'model_fit'
predict_quantile(
  object,
  new_data,
  quantile = (1:9)/10,
  interval = "none",
  level = 0.95,
  ...
)

## S3 method for class 'model_fit'
predict_survival(
  object,
  new_data,
  eval_time,
  time = deprecated(),
  interval = "none",
  level = 0.95,
  ...
)

predict_survival(object, ...)

## S3 method for class '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.

parsnip documentation built on June 24, 2024, 5:14 p.m.