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fitclogitboost<-function(Y,M,groupid,iter=100,rho=0.05){
addfunction.var<- vector("list", iter)
addfunction<- vector("list", iter)
theta<- vector("list", iter)
likeli=c()
win=Y
bigf<-function(x){
10e-5
}
for (k in seq(1,iter)){
if (k<2){
fx=apply(M, 1, bigf)
}
if (k>1){
bigfa<-function(x){
temp=0
for (m in seq(1,k-1)){
temp=temp+rho*theta[[m]]*predict(addfunction[[m]],x[addfunction.var[[m]]])$y
}
return(temp)
}
fx=apply(M, 1, bigfa)
}
grad=persamplegrad(fx,win,length(win),groupid)
diff1=rep(1e10,dim(M)[2])
spllist <- vector("list", dim(M)[2])
predictlist<-vector("list", dim(M)[2])
for(j in seq(1,dim(M)[2])){
spl<- smooth.spline(M[,j],grad)
spl.predict<-predict(spl,M[,j])$y
diff1[j]=sum((grad-spl.predict)^2)
spllist[[j]]=spl
predictlist[[j]]=spl.predict
}
addfunction.var[[k]]=which(diff1==min(diff1))
addfunction[[k]]=spllist[[which(diff1==min(diff1))]]
addfunction.predict=predictlist[[which(diff1==min(diff1))]]
theta[[k]]=uniclogit(addfunction.predict,win,fx,length(win),groupid)
likeli=c(likeli,likelihood(fx,win,length(win),groupid))
}
temp=0
tempm=matrix(0,ncol=dim(M)[2],nrow=length(groupid))
for (d in seq(1,dim(M)[2])){
for (j in seq(1,length(groupid))){
temp=0
x=M[j,d]
for (m in seq(1,length(theta))){
if (addfunction.var[[m]]==d) {
temp=temp+rho*theta[[m]]*predict(addfunction[[m]],x,deriv=1)$y
}}
tempm[j,d]=temp
}
}
ederivxj=apply(tempm^2,2,mean)
varxj=apply(M,2,var)
rinf=sqrt(ederivxj*varxj)
mmax=apply(M,2,max)
mmin=apply(M,2,min)
return(list(func=addfunction,index=addfunction.var,theta=theta,likeli=likeli,rinf=rinf,rho=rho,xmax=mmax,xmin=mmin))
}
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