if (FALSE) {
## cd4 data
require(refund)
data(cd4)
n <- nrow(cd4)
T <- ncol(cd4)
id <- rep(1:n,each=T)
t <- rep(-18:42,times=n)
y <- as.vector(t(cd4))
sel <- which(is.na(y))
## organize data
data <- data.frame(y=log(y[-sel]),
argvals = t[-sel],
subj = id[-sel])
data <- data[data$y>4.5,]
## smooth
fit <- pspline(data)
## plot
plot(data$argvals,fit$mu.new,type="p")
## prediction
pred <- predict(fit,quantile(data$argvals,c(0.2,0.6)))
pred
}
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