proportional_hazards: Proportional hazards regression

View source: R/proportional_hazards.R

proportional_hazardsR Documentation

Proportional hazards regression


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


  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.

  • 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.


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.

References, Tidy Modeling with R, searchable table of parsnip models

See Also




proportional_hazards(mode = "censored regression")

parsnip documentation built on June 24, 2024, 5:14 p.m.