ridge: Ridge regression

In survival: Survival Analysis

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.

Note

If the expression ridge(x1, x2, x3, ...) is too many characters long then the internal terms() function will add newlines to the variable name and then the coxph routine simply gets lost. (Some labels will have the newline and some won't.) One solution is to bundle all of the variables into a single matrix and use that matrix as the argument to ridge so as to shorten the call, e.g. mdata$many <- as.matrix(mydata[,5:53]).

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

survival documentation built on June 22, 2024, 10:49 a.m.