proportional_hazards() is a way to generate a specification of a model
before fitting and allows the model to be created using different packages
in R. The main arguments for the model are:
penalty: The total amount of regularization
in the model. Note that this must be zero for some engines.
mixture: The mixture amounts of different types of
regularization (see below). Note that this will be ignored for some engines.
These arguments are converted to their specific names at the
time that the model is fit. Other options and arguments can be
set_engine(). If left to their defaults
NULL), the values are taken from the underlying model
functions. If parameters need to be modified,
update() can be used
in lieu of recreating the object from scratch.
proportional_hazards( mode = "censored regression", engine = "survival", penalty = NULL, mixture = NULL )
A single character string for the prediction outcome mode. Possible values for this model are "unknown", or "censored regression".
A single character string specifying what computational engine
to use for fitting. Possible engines are listed below. The default for this
A non-negative number representing the total amount of regularization (specific engines only).
A number between zero and one (inclusive) that is the
proportion of L1 regularization (i.e. lasso) in the model. When
Proportional hazards models include the Cox model.
proportional_hazards(), the mode will always be "censored regression".
show_engines("proportional_hazards")#> # A tibble: 0 × 2 #> # … with 2 variables: engine <chr>, mode <chr>