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

 CoxModel 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 `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 and further model details can be found in the source 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.

`coxph`, `coxph.control`, `stepAIC`, `fit`, `resample`

### Examples

```library(survival)

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

```

MachineShop documentation built on Sept. 5, 2022, 5:08 p.m.