# CoxModel: Proportional Hazards Regression Model In MachineShop: Machine Learning Models and Tools

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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 ) ```

## 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 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.

`coxph`, `coxph.control`, `stepAIC`, `fit`, `resample`
 ```1 2 3``` ```library(survival) fit(Surv(time, status) ~ ., data = veteran, model = CoxModel) ```