General Interface for Parametric Survival Models
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.
If parameters need to be modified, this function can be used in lieu of recreating the object from scratch.
surv_reg(mode = "regression", dist = NULL) ## S3 method for class 'surv_reg' update(object, parameters = NULL, dist = NULL, fresh = FALSE, ...)
mode |
A single character string for the type of model. The only possible value for this model is "regression". |
dist |
A character string for the outcome distribution. "weibull" is the default. |
object |
A survival regression model specification. |
parameters |
A 1-row tibble or named list with main
parameters to update. If the individual arguments are used,
these will supersede the values in |
fresh |
A logical for whether the arguments should be modified in-place of or replaced wholesale. |
... |
Not used for |
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() 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).
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 pre-set 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()
## 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()
## 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 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.
| 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.
show_engines("surv_reg")
surv_reg()
# Parameters can be represented by a placeholder:
surv_reg(dist = varying())
model <- surv_reg(dist = "weibull")
model
update(model, dist = "lnorm")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.