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

fit1 <- coxph(Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta=1),
ovarian)
fit1
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)
lfit0
lfit1
lfit2
lfit3

[Package

*survival* version 2.20

Index]