R: Ridge regression

ridge {survival} | R Documentation

## Ridge regression

### Description

When used in a coxph or survreg model formula, specifies a ridge regression term. The likelihood is penalised by `theta`/2 time the sum of squared coefficients. If `scale=T` the penalty is calculated for coefficients based on rescaling the predictors to have unit variance. If `df` is specified then `theta` is chosen based on an approximate degrees of freedom.

### Usage

    ridge(..., theta, df=nvar/2, eps=0.1, scale=TRUE)

### Arguments

`...` | predictors to be ridged
`theta` | penalty is `theta`/2 time sum of squared coefficients
`df` | Approximate degrees of freedom
`eps` | Accuracy required for `df`
`scale` | Scale variables before applying penalty?

### Value

An object of class `coxph.penalty` containing the data and control functions.

### References

Gray (1992) "Flexible methods of analysing survival data using splines, with applications to breast cancer prognosis" JASA 87:942–951

### See Also

`coxph`,`survreg`,`pspline`,`frailty`

### Examples

    coxph(Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta=1),
    	      ovarian)

    lfit0 <- survreg(Surv(time, status) ~1, cancer)
    lfit1 <- survreg(Surv(time, status) ~ age + ridge(ph.ecog, theta=5), cancer)
    lfit2 <- survreg(Surv(time, status) ~ sex + ridge(age, ph.ecog, theta=1), cancer)
    lfit3 <- survreg(Surv(time, status) ~ sex + age + ph.ecog, cancer)

[Package survival version 2.37-7 Index]