proportional_hazards() defines a model for the hazard function
as a multiplicative function of covariates times a baseline hazard. This
function can fit censored regression models.
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
proportional_hazards( mode = "censored regression", engine = "survival", penalty = NULL, mixture = NULL )
A single character string for the prediction outcome mode. The only possible value for this model is "censored regression".
A single character string specifying what computational engine to use for fitting.
A non-negative number representing the total amount of regularization (specific engines only).
A number between zero and one (inclusive) denoting the proportion of L1 regularization (i.e. lasso) in the model.
Available for specific engines only.
This function only defines what type of model is being fit. Once an engine
is specified, the method to fit the model is also defined. See
set_engine() for more on setting the engine, including how to set engine
The model is not trained or fit until the
fit() function is used
with the data.
Since survival models typically involve censoring (and require the use of
survival::Surv() objects), the
fit.model_spec() function will require that the
survival model be specified via the formula interface.
Proportional hazards models include the Cox model.
show_engines("proportional_hazards") proportional_hazards(mode = "censored regression")
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