These are engines for the
parsnip::survival_reg() model specification.
survival_reg() is a way to generate a specification of a model
before fitting and allows the model to be created using
R. The main argument for the
dist: The probability distribution of the outcome.
This argument is converted to its specific names at the
time that the model is fit. Other options and argument can be
set_engine(). If left to its default
NULL), the value is taken from the underlying model
Since survival models typically involve censoring (and require the use of
survival::Surv() objects), the
fit() function will require that the
survival model be specified via the formula interface.
Also, for the
flexsurv::flexsurvfit engine, the typical
strata function cannot be used. To achieve the same effect,
the extra parameter roles can be used (as described above).
The model can be created using the
fit() function using the
"survival" (the default)
Engines may have pre-set default arguments when executing the model fit call. For this type of model, the template of the fit calls are below.
survival_reg() %>% set_engine("flexsurv") %>% set_mode("censored regression") %>% translate()
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## Parametric Survival Regression Model Specification (censored regression) ## ## Computational engine: flexsurv ## ## Model fit template: ## flexsurv::flexsurvreg(formula = missing_arg(), data = missing_arg(), ## weights = missing_arg())
survival_reg() %>% set_engine("survival") %>% set_mode("censored regression") %>% translate()
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## Parametric Survival Regression Model Specification (censored regression) ## ## Computational engine: survival ## ## Model fit template: ## survival::survreg(formula = missing_arg(), data = missing_arg(), ## weights = missing_arg(), model = TRUE)
model = TRUE is needed to produce quantile predictions when
there is a stratification variable and can be overridden in other cases.
fit() passes the data directly to
survival::curvreg() so that its
formula method can create dummy variables as-needed.
The standardized parameter names in parsnip can be mapped to their original names in each engine that has main parameters. Each engine typically has a different default value (shown in parentheses) for each parameter.
Jackson, C. (2016).
flexsurv: A Platform for Parametric Survival
Modeling in R. Journal of Statistical Software, 70(8), 1 - 33.
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