CoxModel: Proportional Hazards Regression Model

In MachineShop: Machine Learning Models and Tools

Proportional Hazards Regression Model

Description

Fits a Cox proportional hazards regression model. Time dependent variables, time dependent strata, multiple events per subject, and other extensions are incorporated using the counting process formulation of Andersen and Gill.

Usage

CoxModel(ties = c("efron", "breslow", "exact"), ...)

CoxStepAICModel(
  ties = c("efron", "breslow", "exact"),
  ...,
  direction = c("both", "backward", "forward"),
  scope = list(),
  k = 2,
  trace = FALSE,
  steps = 1000
)

Arguments

ties	character string specifying the method for tie handling.
...	arguments passed to coxph.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.

In calls to varimp for CoxModel and CoxStepAICModel, numeric argument base may be specified for the (negative) logarithmic transformation of p-values [defaul: exp(1)]. Transformed p-values are automatically scaled in the calculation of variable importance to range from 0 to 100. To obtain unscaled importance values, set scale = FALSE.

Value

MLModel class object.

See Also

coxph, coxph.control, stepAIC, fit, resample

Examples

library(survival)

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

MachineShop documentation built on Sept. 11, 2024, 6:28 p.m.