CoxModel {MachineShop} | R Documentation |
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 |
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. |
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)