krylow.pls {timereg}

Fits Krylow based PLS for additive hazards model

Description

Fits the PLS estimator for the additive risk model based on the least squares fitting criterion

Usage

krylow.pls(D, d, dim = 1)

Arguments

D

defined above

d

defined above

dim

number of pls dimensions

Details

L(\beta,D,d) = \beta^T D \beta - 2 \beta^T d

where D=\int Z H Z dt and d=\int Z H dN.

Value

returns a list with the following arguments:

beta

PLS regression coefficients

Author(s)

Thomas Scheike

References

Martinussen and Scheike, The Aalen additive hazards model with high-dimensional regressors, submitted.

Martinussen and Scheike, Dynamic Regression Models for Survival Data, Springer (2006).

Examples

## makes data for pbc complete case
data(mypbc)
pbc<-mypbc
pbc$time<-pbc$time+runif(418)*0.1; pbc$time<-pbc$time/365
pbc<-subset(pbc,complete.cases(pbc));
covs<-as.matrix(pbc[,-c(1:3,6)])
covs<-cbind(covs[,c(1:6,16)],log(covs[,7:15]))

## computes the matrices needed for the least squares 
## criterion 
out<-aalen(Surv(time,status>=1)~const(covs),pbc,robust=0,n.sim=0)
S=out$intZHZ; s=out$intZHdN;

out<-krylow.pls(S,s,dim=2)