You should definitely read Loader's book. Anyway, in the meantime, you should look an introductory paper that you will find at the Locfit web page. I think that you can set Locfit to estimate at all the sample points, which it does not by default, and also to use a prespecified constant bandwidth, but notice that its definition of the h parameter is not the standard one.
Hope this helps, Miguel A. On Thursday 14 April 2005 10:47, Jacho-Chavez,DT (pgr) wrote: > Dear R-users, > > One of the main reasons I moved from GAUSS to R (as an econometrician) was > because of the existence of the library LOCFIT for local polynomial > regression. While doing some checking between my former `GAUSS code' and my > new `R code', I came to realize LOCFIT is not quite doing what I want. I > wrote the following example script: > > #-------------------------------------------------------------------------- >--------------------------------------- # Plain Vanilla NADARAYA-WATSON > estimator (or Local Constant regression, e.g. deg=0) # with gaussian kernel > & fixed bandwidth > > mkern<-function(y,x,h){ > Mx <- matrix(x,nrow=length(y),ncol=length(y),byrow=TRUE) > Mxh <- (1/h)*dnorm((x-Mx)/h) > Myxh<- (1/h)*y*dnorm((x-Mx)/h) > yh <- rowMeans(Myxh)/rowMeans(Mxh) > return(yh) > } > > # Generating the design Y=m(x)+e > n <- 10 > h <- 0.5 > x <- rnorm(n) > y <- x + rnorm(n,mean=0,sd=0.5) > > # This is what I really want! > mhat <- mkern(y,x,h) > > library(locfit) > yhl.raw <- > locfit(y~x,alpha=c(0,h),kern="gauss",ev="data",deg=0,link="ident") > > # This is what I get with LOCFIT > print(cbind(x,mhat,residuals(yhl.raw,type="fit"),knots(yhl.raw,what="coef") >)) > #-------------------------------------------------------------------------- >------------------------------------------ > > Questions: > 1) Why are residuals(.) & knots(.) results different from one another? If I > want m^(x[i]) at each evaluation point i=1,...,n, which one should I use? I > do not want interpolation whatsoever. 2) Why are they `close' but not equal > to what I want? > > I can accept differences for higher degrees and multidimensional data at > the boundary of the support (given the way we must do the regression in > areas with sparse data) But why are these difference present for deg=0 > inside the support as well as at the boundary? The computer would still > give us a result even with a close-to-zero random denominator (admittedly, > not a reliable one). Unfortunately, I cannot get access to a copy of > "Loader, C. (1999) Local Regression and Likelihood, Springer" from my local > library, so a small explanation or advice would be greatly appreciated. > > I do not mind using an improved version of `what I want', but I would like > to understand what am I doing? > > > Thanks in advanced for your help, > > > David Jacho-Chávez > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html