krylow.pls | R Documentation |
Fits the PLS estimator for the additive risk model based on the least squares fitting criterion
krylow.pls(D, d, dim = 1)
D |
defined above |
d |
defined above |
dim |
number of pls dimensions |
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.
returns a list with the following arguments:
beta |
PLS regression coefficients |
Thomas Scheike
Martinussen and Scheike, The Aalen additive hazards model with high-dimensional regressors, submitted.
Martinussen and Scheike, Dynamic Regression Models for Survival Data, Springer (2006).
## 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)
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