# krylow.pls: Fits Krylow based PLS for additive hazards model In timereg: Flexible Regression Models for Survival Data

## Description

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

## Usage

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

## Arguments

 D defined above d defined above dim number of pls dimensions

## Details

L(β,D,d) = β^T D β - 2 β^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

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

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 ## 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 May 29, 2017, 2 p.m.

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