Description Usage Arguments Details Engine Details References See Also Examples
This function is softdeprecated in favor of survival_reg()
which uses the
"censored regression"
mode.
surv_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
model is:
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 arguments can be
set using set_engine()
. If left to its default
here (NULL
), the value is taken from the underlying model
functions.
The data given to the function are not saved and are only used
to determine the mode of the model. For surv_reg()
,the
mode will always be "regression".
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.
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).
1 
mode 
A single character string for the prediction outcome mode.
The only possible value for this model is "regression".
@param engine A single character string specifying what computational engine
to use for fitting. Possible engines are listed below. The default for this
model is 
dist 
A character string for the outcome distribution. "weibull" is the default. 
For surv_reg()
, the mode will always be "regression".
The model can be created using the fit()
function using the
following engines:
R: "flexsurv"
, "survival"
(the default)
Engines may have preset default arguments when executing the model fit call. For this type of model, the template of the fit calls are below.
surv_reg() %>% set_engine("flexsurv") %>% set_mode("regression") %>% translate()
1 2 3 4 5 6 7 8 9 10  ## Warning: `surv_reg()` was deprecated in parsnip 0.1.6.
## Please use `survival_reg()` instead.
## Parametric Survival Regression Model Specification (regression)
##
## Computational engine: flexsurv
##
## Model fit template:
## flexsurv::flexsurvreg(formula = missing_arg(), data = missing_arg(),
## weights = missing_arg())

surv_reg() %>% set_engine("survival") %>% set_mode("regression") %>% translate()
1 2 3 4 5 6 7 8 9 10  ## Warning: `surv_reg()` was deprecated in parsnip 0.1.6.
## Please use `survival_reg()` instead.
## Parametric Survival Regression Model Specification (regression)
##
## Computational engine: survival
##
## Model fit template:
## survival::survreg(formula = missing_arg(), data = missing_arg(),
## weights = missing_arg(), model = TRUE)

Note that 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 asneeded.
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.
parsnip  flexsurv  survival 
dist  dist  dist 
Jackson, C. (2016). flexsurv
: A Platform for Parametric Survival
Modeling in R. Journal of Statistical Software, 70(8), 1  33.
fit.model_spec()
, survival::Surv()
, set_engine()
, update()
1 2 3  show_engines("surv_reg")
surv_reg()

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