Dear all,
I need to calculate likelihood ratio test for ridge regression. In February I
have reported a bug where coxph returns unpenalized log-likelihood for final
beta estimates for ridge coxph regression. In high-dimensional settings ridge
regression models usually fail for lower values of lambda. As the result of it,
in such settings the ridge regressions have higher values of lambda (e.g. over
100) which means that the difference between unpenalized log-likelihood and
penalized log-likelihood is not insignificant. I would be grateful if someone
can confirm that the below code is correct workaround.
ridge.penalty <- function(theta,beta)
{
(theta/2)*sum(beta^2)
}
a.theta <- 100
fit1 <- coxph(Surv(futime, fustat) ~ ridge(rx,age,ecog.ps,theta=a.theta),
data=ovarian)
# correct loglikelihood for final beta estimate
ridge.loglikelihood.beta <- coxph$loglik[2] - ridge.penalty(a.theta, fit1$coef)
Thanks in advance
DK
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