details_survival_reg_flexsurv: Parametric survival regression

details_survival_reg_flexsurvR Documentation

Parametric survival regression

Description

flexsurv::flexsurvreg() fits a parametric survival model.

Details

For this engine, there is a single mode: censored regression

Tuning Parameters

This model has 1 tuning parameters:

  • dist: Distribution (type: character, default: ‘weibull’)

Translation from parsnip to the original package

The censored extension package is required to fit this model.

library(censored)

survival_reg(dist = character(1)) %>% 
  set_engine("flexsurv") %>% 
  set_mode("censored regression") %>% 
  translate()
## Parametric Survival Regression Model Specification (censored regression)
## 
## Main Arguments:
##   dist = character(1)
## 
## Computational engine: flexsurv 
## 
## Model fit template:
## flexsurv::flexsurvreg(formula = missing_arg(), data = missing_arg(), 
##     weights = missing_arg(), dist = character(1))

Other details

The main interface for this model uses the formula method since the model specification typically involved the use of survival::Surv().

For this engine, stratification cannot be specified via strata(), please see flexsurv::flexsurvreg() for alternative specifications.

Predictions of type "time" are predictions of the mean survival time.

Case weights

This model can utilize case weights during model fitting. To use them, see the documentation in case_weights and the examples on tidymodels.org.

The fit() and fit_xy() arguments have arguments called case_weights that expect vectors of case weights.

Saving fitted model objects

This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.

References

  • Jackson, C. 2016. flexsurv: A Platform for Parametric Survival Modeling in R. Journal of Statistical Software, 70(8), 1 - 33.


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