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MGLVC.ft=function(data.k1.ord,res.cv,span,n){
x=data.k1.ord$X
d=data.k1.ord$D
y=data.k1.ord$Y
ipw=data.k1.ord$ipw
y.cv=res.cv$y
x.unq=unique(x)
M=length(x.unq);
nk=length(x) #sample for kth fold
lik=rep(NA,nk)
for(i in 1:nk){
#(1) compute wt evt and tot
y.span=(y-y[i])/span
num.wt=dnorm(y.span)
den.wt=sum(num.wt)
wt=num.wt/den.wt
dwt=wt*ipw #double weight
evt=tot=rep(NA,M)
for(m in 1:M){
evt[m]=sum((x.unq[m]==x & d==1)*dwt)
tot[m]=sum((x.unq[m]==x)*dwt)
}
#(2) nearest point: j(i)
ji=which.min(abs(y[i]-y.cv))
res.ji=res.cv[[ji]]
F.ji=res.ji$F
x.ji=res.ji$x
#(3) F.ji is defined on x.unq by right-cont assumption
F.tilder=rep(0,M)
if(x.unq[1]<x.ji[1]){
m.idx1=max(which(x.unq<x.ji[1]))
F.tilder[1:m.idx1]=0
for(m in (m.idx1+1):M)
F.tilder[m]=F.ji[which.max(which(x.ji<=x.unq[m]))]
}else{
for(m in 1:M)
F.tilder[m]=F.ji[which.max(which(x.ji<=x.unq[m]))]
}
#(4) compute log lik
idx=which(0<F.tilder & F.tilder<1) #to prevent lik is infinite
lik[i]=sum((evt*log(F.tilder)+(tot-evt)*log(1-F.tilder))[idx]) #lik
}
lik2=sum(lik)
return(lik2)
}
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