| ridge {survival} | R Documentation |
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.
ridge(..., theta, df=nvar/2, eps=0.1, scale=TRUE)
... |
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? |
An object of class coxph.penalty containing the data and
control functions.
Gray (1992) "Flexible methods of analysing survival data using splines, with applications to breast cancer prognosis" JASA 87:942–951
data(ovarian)
fit1 <- coxph(Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta=1),
ovarian)
fit1
data(cancer)
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