survival::survreg() 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("survival") %>%
set_mode("censored regression") %>%
translate()## Parametric Survival Regression Model Specification (censored regression)
##
## Main Arguments:
## dist = character(1)
##
## Computational engine: survival
##
## Model fit template:
## survival::survreg(formula = missing_arg(), data = missing_arg(),
## weights = missing_arg(), dist = character(1), model = TRUE)Other details
In the translated syntax above, note that model = TRUE is needed to
produce quantile predictions when there is a stratification variable and
can be overridden in other cases.
The main interface for this model uses the formula method since the
model specification typically involved the use of
survival::Surv().
The model formula can include special terms, such as
survival::strata(). The allows the model scale
parameter to differ between groups contained in the function. The column
used inside strata() is treated as qualitative no matter its type. To
learn more about using special terms in formulas with tidymodels, see
?model_formula.
For example, in this model, the numeric column rx is used to estimate
two different scale parameters for each value of the column:
library(survival)
survival_reg() %>%
fit(Surv(futime, fustat) ~ age + strata(rx), data = ovarian) %>%
extract_fit_engine()## Call:
## survival::survreg(formula = Surv(futime, fustat) ~ age + strata(rx),
## data = data, model = TRUE)
##
## Coefficients:
## (Intercept) age
## 12.8734120 -0.1033569
##
## Scale:
## rx=1 rx=2
## 0.7695509 0.4703602
##
## Loglik(model)= -89.4 Loglik(intercept only)= -97.1
## Chisq= 15.36 on 1 degrees of freedom, p= 8.88e-05
## n= 26Predictions 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.