Proportional Hazards Regression Model
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 = NULL,
k = 2,
trace = FALSE,
steps = 1000
)ties |
character string specifying the method for tie handling. |
... |
arguments passed to |
direction |
mode of stepwise search, can be one of |
scope |
defines the range of models examined in the stepwise search.
This should be a list containing components |
k |
multiple of the number of degrees of freedom used for the penalty.
Only |
trace |
if positive, information is printed during the running of
|
steps |
maximum number of steps to be considered. |
Surv
Default values for the NULL arguments and further model details can be
found in the source link 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.
#' @return MLModel class object.
library(survival) fit(Surv(time, status) ~ ., data = veteran, model = CoxModel)
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