cox.ridge: Fit a Cox model with a ridge penalty on all covariates
In CoxRidge: Cox Models with Dynamic Ridge Penalties

Description Usage Arguments Value Note Author(s) References See Also Examples

Fits a simple Cox model with a ridge penalty on all coefficients. The penalty weight can be optimized using a REML-type likelihood method or be chosen by the user.

1 2	cox.ridge(formula, lambda = 1, lambdaFixed = FALSE, eps = 10e-6, data = sys.parent(), iter.max = 200, mon = FALSE)

`formula`	a formula object, with the response on the left of a '~' operator, and the terms on the right. The response must be a survival object as returned by the 'Surv' function.
`lambdaFixed`	when TRUE the function does not seek to optimize the penalty weight.
`lambda`	When lambdaFixed is FALSE lambda is a scalar giving the starting value for the weight of the penalty. When lambdaFixed is true lambda is the chosen weight of the penalty.
`eps`	a small value. The criterion of convergance.
`data`	an optional data frame containing the variables named in the formula.
`iter.max`	maximum number of iterations, default is 200.
`mon`	when true the function prints out the computed lambda weigh in each iteration.

cox.ridge returns an object of class "cox.ridge" The function print.cox.ridge is used to obtain and print a summary of the results. An object of class "cox.ridge" is a list containing the following components:

`call`	function call.
`coef`	the vector of coefficients.
`loglik`	the penalized log-likelihood of the model.
`time`	a vector with failure/censoring times.
`death`	a vector of status indicator.
`X`	a matrix of covariates.
`iter`	number of iterations used to maximise likelihood at a fixed lambda.
`inter.it`	number of iterations used to find optimal lambda.'
`lambda`	optimal weight of the penalty.
`Hat`	the hat matrix at convergance.
`hess`	the Hessian matrix of second derivatives.

The function at the current form cannot handle missing values. The user has to take prior action with missing values before using this function.

Aris Perperoglou

Perperoglou A.(2013)Cox models with dynamic ridge penalties on time varying effects of the covariates. Statistics in Medicine, to appear

coxph, Dynamic.Ridge

data(ova)
attach(ova)
X <- cbind(karn,diam,figo)
X <- apply(X,2,function(x){(x-mean(x))/sqrt(var(x))})#standardize covariates
fit <- cox.ridge(Surv(time,death)~X,lambda=1)
fit ##regression coefficients correspond to the standardized covariates

Loading required package: survival
Loading required package: splines
call:
cox.ridge(formula = Surv(time, death) ~ X, lambda = 1)

        coef exp(coef)
Xkarn 0.2035    1.2257
Xdiam 0.2617    1.2992
Xfigo 0.2298    1.2583

penalized log-likelihood=  -1383.731
Optimal penalty weight=  17.22639 (converged in 6 internal iterations)
Algorithm converged in 12 iterations