SurvRegModel: Parametric Survival Model

In brian-j-smith/MachineShop: Machine Learning Models and Tools

Parametric Survival Model

Description

Fits the accelerated failure time family of parametric survival models.

Usage

SurvRegModel(
  dist = c("weibull", "exponential", "gaussian", "logistic", "lognormal",
    "logloglogistic"),
  scale = 0,
  parms = list(),
  ...
)

SurvRegStepAICModel(
  dist = c("weibull", "exponential", "gaussian", "logistic", "lognormal",
    "logloglogistic"),
  scale = 0,
  parms = list(),
  ...,
  direction = c("both", "backward", "forward"),
  scope = list(),
  k = 2,
  trace = FALSE,
  steps = 1000
)

Arguments

dist	assumed distribution for y variable.
scale	optional fixed value for the scale.
parms	list of fixed parameters.
...	arguments passed to survreg.control.
direction	mode of stepwise search, can be one of "both" (default), "backward", or "forward".
scope	defines the range of models examined in the stepwise search. This should be a list containing components upper and lower, both formulae.
k	multiple of the number of degrees of freedom used for the penalty. Only k = 2 gives the genuine AIC; k = .(log(nobs)) is sometimes referred to as BIC or SBC.
trace	if positive, information is printed during the running of stepAIC. Larger values may give more information on the fitting process.
steps	maximum number of steps to be considered.

Details

Response types:

Surv

Default argument values and further model details can be found in the source See Also links below.

Value

MLModel class object.

See Also

psm, survreg, survreg.control, stepAIC, fit, resample

stepAIC, fit, resample

Examples

## Requires prior installation of suggested packages rms and Hmisc to run

library(survival)

fit(Surv(time, status) ~ ., data = veteran, model = SurvRegModel)

brian-j-smith/MachineShop documentation built on Sept. 22, 2023, 10:01 p.m.