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 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_levels = NULL,
quantile = deprecated(),
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
A model fit.
- new_data
A rectangular data object, such as a data frame.
- ...
Additional
parsnip
-related options, depending on the value oftype
. Arguments to the underlying model's prediction function cannot be passed here (use theopts
argument instead). Possible arguments are:interval
: fortype
equal to"survival"
or"quantile"
, should interval estimates be added, if available? Options are"none"
and"confidence"
.level
: fortype
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 is0.95
.std_error
: fortype
equal to"conf_int"
or"pred_int"
, add the standard error of fit or prediction (on the scale of the linear predictors). Default value isFALSE
.quantile
: fortype
equal toquantile
, the quantiles of the distribution. Default is(1:9)/10
.eval_time
: fortype
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, quantile_levels
A vector of values between 0 and 1 for the quantile to be predicted. If the model has a
"quantile regression"
mode, this value should beNULL
. For other modes, the default is(1:9)/10
. Note that, as of version 1.3.0 of parsnip, thequantile
is deprecated. Usequantile_levels
instead.