Nothing
pp.regression<-function(x,y=NULL,arg=NULL,residu=NULL,teet=NULL,
h=1,kernel="gauss",M=2,method="poid",argd=NULL,vect=FALSE,seed=1)
{
set.seed(seed)
obsit<-ceiling(runif(M)*n)
d<-dim(x)[2]
n<-dim(x)[1]
if (is.null(residu)){
residu<-matrix(0,n,M)
teet<-matrix(0,M,d)
eval<-matrix(0,n,1)
for (m in 1:M){
residu[,m]<-y-eval
ycur<-y-eval
obs<-obsit[m]
argd<-x[obs,]
theta<-single.index(x,ycur,h=h,method=method,argd=argd)
teet[m,]<-theta
xcur<-x%*%theta
if (!vect){
estimat<-matrix(0,n,1)
for (nn in 1:n){
#arg<-x[nn,]
#arg<-matrix(arg,d,1)
#acur<-sum(arg*theta)
acur<-xcur[nn]
est<-kernesti.regr(acur,xcur,ycur,h=h,kernel=kernel)
estimat[nn]<-est
}
}
else{
estimat<-kernesti.regr(xcur,xcur,ycur,h=h,kernel=kernel,vect=vect)
}
eval<-eval+estimat
}
}
else eval<-NULL
if (is.null(arg)){
value<-NULL
}
else{
value<-0
for (m in 1:M){
ycur<-matrix(residu[,m],n,1)
tcur<-matrix(teet[m,],d,1)
xcur<-x%*%tcur
arg<-matrix(arg,d,1)
carg<-sum(arg*tcur)
w<-kernesti.weights(carg,xcur,h=h,kernel=kernel)
curvalue<-t(w)%*%ycur
value<-value+curvalue
}
}
return(list(eval=eval,residu=residu,teet=teet,value=value))
}
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