Make predictions from a recursive model.
# S3 method for recursive
predict(object, new_data, type = NULL, opts = list(), ...)An object of class model_fit
A rectangular data object, such as a data frame.
A single character value or NULL. Possible values
are "numeric", "class", "prob", "conf_int", "pred_int", "quantile",
or "raw". When NULL, predict() will choose an appropriate value
based on the model's mode.
A list of optional arguments to the underlying
predict function that will be used when type = "raw". The
list should not include options for the model object or the
new data being predicted.
Arguments to the underlying model's prediction
function cannot be passed here (see opts). There are some
parsnip related options that can be passed, depending on the
value of type. Possible arguments are:
level: for types of "conf_int" and "pred_int" 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: add the standard error of fit or prediction (on
the scale of the linear predictors) for types of "conf_int"
and "pred_int". Default value is FALSE.
quantile: the quantile(s) for quantile regression
(not implemented yet)
time: the time(s) for hazard probability estimates
(not implemented yet)
Refer to recursive() for further details and examples.