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
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.
predictors to be ridged
Approximate degrees of freedom
Accuracy required for
Scale variables before applying penalty?
An object of class
coxph.penalty containing the data and
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,
mdata$many <- as.matrix(mydata[,5:53]).
Gray (1992) "Flexible methods of analysing survival data using splines, with applications to breast cancer prognosis" JASA 87:942–951
1 2 3 4 5 6 7
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.