Nothing
pnr <- function(resp, bw.g = NULL, alpha = .5){
#pnr = poly.non.ram
N<-dim(resp)[1]
I<-dim(resp)[2]
opt <- as.numeric(levels(factor(resp)))
M <- length(opt)
tots <- rowSums(resp, na.rm=T)
high <- max(tots, na.rm=T)
low <- min(tots, na.rm=T)
x.points <- low:high
n.x <- length(x.points)
density <- hist(tots, breaks = seq(low-.5,(high+.5), by=1), plot=FALSE)$density
kden <- density(tots, bw = "nrd", n = n.x, from = low, to = high)
if(is.null(bw.g)){
bw.g <- bw.nrd(tots)
}
#adaptive bandwidth
g.mean <- exp(mean(log(kden$y)))
lambda <- (kden$y/g.mean)^-alpha
ks<-array(NA, dim = c(n.x, M, I ) )
for(j in 1:I) {
ts.j <- tots
for(m in 1:M){
an <-opt[m]
temp <- ifelse(resp[,j]==an, temp<-1, temp<-0)
for(i in 1:n.x){
ks[ i, m, j] <- sum(
exp(-((ts.j - x.points[i])/(bw.g*lambda[i]))^2/2)
*temp) /
sum(
exp(-((ts.j - x.points[i])/(bw.g*lambda[i]))^2/2)
)
}}}
return(list(x.points, density, ks))
}
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