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.
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
)
MLModel
class object.
character string specifying the method for tie handling.
arguments passed to coxph.control
.
mode of stepwise search, can be one of "both"
(default), "backward"
, or "forward"
.
defines the range of models examined in the stepwise search.
This should be a list containing components upper
and lower
,
both formulae.
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.
if positive, information is printed during the running of
stepAIC
. Larger values may give more information on the fitting
process.
maximum number of steps to be considered.
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
.
coxph
,
coxph.control
, stepAIC
,
fit
, resample
library(survival)
fit(Surv(time, status) ~ ., data = veteran, model = CoxModel)
Run the code above in your browser using DataLab