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cv.cox.interaction=function(x, trt, y, status, K.cv=5, num.replicate=1, nsteps, mincut=0.1, backfit=F, maxnumcut=1, dirp=0){
x=as.matrix(x)
n=length(y)
sc.tot=matrix(0, nrow=K.cv, ncol=nsteps)
preval.tot=matrix(0, nrow=nsteps, ncol=n)
for(b in 1:num.replicate)
{g=sample(rep(1:K.cv,(n+K.cv-1)/K.cv))
g=g[1:n]
cva=vector("list", K.cv)
sc=matrix(0, nrow=K.cv, ncol=nsteps)
preval=matrix(0, nrow=nsteps, ncol=n)
pv.fit=rep(0, nsteps)
for(i in 1:K.cv)
{cva[[i]]=cox.interaction(x[g!=i ,], trt[g!=i], y[g!=i], status[g!=i], nsteps=nsteps, mincut=mincut, backfit=backfit, maxnumcut=maxnumcut, dirp=dirp)
for(ii in 1:nsteps)
{aa=index.prediction(cva[[i]]$res[[ii]],x[g==i,])
fit=coxph(Surv(y[g==i],status[g==i])~aa*trt[g==i])
sc[i,ii]=summary(fit)$coef[3,4]
preval[ii,g==i]=aa
}
}
sc.tot=abs(sc.tot)+sc
preval.tot=preval.tot+preval
}
meansc=colMeans(sc.tot)
kmax=which.max(meansc)
pvfit.score=rep(0, nsteps)
for(i in 1:nsteps)
{fit=coxph(Surv(y,status)~preval.tot[i,]*trt)
pvfit.score[i]=summary(fit)$coef[3,4]
}
return(list(kmax=kmax, meanscore=meansc/num.replicate, pvfit.score=pvfit.score, preval=preval.tot/num.replicate))
}
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