details_survival_reg_survival | R Documentation |
survival::survreg()
fits a parametric survival model.
For this engine, there is a single mode: censored regression
This model has 1 tuning parameters:
dist
: Distribution (type: character, default: â€˜weibullâ€™)
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)
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= 26
Predictions of type "time"
are predictions of the mean survival time.
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.
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.
Kalbfleisch, J. D. and Prentice, R. L. 2002 The statistical analysis of failure time data, Wiley.
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