`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 using `set_engine()`

. If left to their defaults
here (`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",
penalty = NULL,
mixture = NULL
)

## Arguments

mode |
A single character string for the type of model.
Possible values for this model are "unknown", or "censored regression". |

penalty |
A non-negative number representing the total
amount of regularization (`glmnet` , `keras` , and `spark` only).
For `keras` models, this corresponds to purely L2 regularization
(aka weight decay) while the other models can be a combination
of L1 and L2 (depending on the value of `mixture` ; see below). |

mixture |
A number between zero and one (inclusive) that is the
proportion of L1 regularization (i.e. lasso) in the model. When
`mixture = 1` , it is a pure lasso model while `mixture = 0` indicates that
ridge regression is being used. (`glmnet` and `spark` only). |

## Details

Proportional hazards models include the Cox model.
For `proportional_hazards()`

, the mode will always be "censored regression".

## See also

## Examples

#> # A tibble: 0 x 2
#> # … with 2 variables: engine <chr>, mode <chr>