Predicted values using using local polynomials
Predicted values from a local polynomials of degree less than 2. See
locpoly
for fast binned implementation
over an equally-spaced grid of local polynomial (gaussian kernel only)
Missing values are not allowed.
## S3 method for class 'npregress' predict(object, newdata, interval= c("none", "confidence", "prediction"), deriv=FALSE, ...)
object |
Object of class |
newdata |
An optional vector of values to be predicted. If omitted, the fitted values are used. |
interval |
Type of interval calculation. Only |
deriv |
Bolean. If |
... |
Further arguments passed to or from other methods. |
Produces a vector of predictions. If deriv
is TRUE
the value is a named list with components: yhat
which contains predictions and (if relevant) deriv
the
first derivative of the local polynomial of degree 1.
Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.
Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.
f <- function(x){sin(5*pi*x)} n <- 100 x <- runif(n) z <- f(x) sigma2 <- 0.05*var(z) erreur<-rnorm(n,0,sqrt(sigma2)) y<-z+erreur grid <- seq(min(x),max(x),length=500) res <- npregress(x,y,bandwidth=0.02,control.par=list(degree=1)) plot(x,y) lines(grid,predict(res,grid))
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