krylow.pls: Fits Krylow based PLS for additive hazards model

In timereg: Flexible Regression Models for Survival Data

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)

timereg documentation built on Sept. 11, 2024, 8:35 p.m.