`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.

There are different ways to fit this model, and the method of estimation is chosen by setting the model *engine*. The engine-specific pages for this model are listed below.

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

## Usage

```
proportional_hazards(
mode = "censored regression",
engine = "survival",
penalty = NULL,
mixture = NULL
)
```

## Arguments

- mode
A single character string for the prediction outcome mode. The only possible value for this model is "censored regression".

- engine
A single character string specifying what computational engine to use for fitting.

- penalty
A non-negative number representing the total amount of regularization (specific engines only).

- mixture
A number between zero and one (inclusive) denoting the proportion of L1 regularization (i.e. lasso) in the model.

`mixture = 1`

specifies a pure lasso model,`mixture = 0`

specifies a ridge regression model, and`0 < mixture < 1`

specifies an elastic net model, interpolating lasso and ridge.

Available for specific engines only.

## Details

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
arguments.

The model is not trained or fit until the `fit()`

function is used
with the data.

Each of the arguments in this function other than `mode`

and `engine`

are
captured as quosures. To pass values
programmatically, use the injection operator like so:

```
value <- 1
proportional_hazards(argument = !!value)
```

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.

## Examples

```
show_engines("proportional_hazards")
#> # A tibble: 0 × 2
#> # ℹ 2 variables: engine <chr>, mode <chr>
proportional_hazards(mode = "censored regression")
#> ! parsnip could not locate an implementation for `proportional_hazards`
#> censored regression model specifications.
#> ℹ The parsnip extension package censored implements support for this
#> specification.
#> ℹ Please install (if needed) and load to continue.
#> Proportional Hazards Model Specification (censored regression)
#>
#> Computational engine: survival
#>
```