Thanks for that suggestion I've investigated a little more using... y <- rowSums(x) + runif(n) ... just so I had some correlation to play with.
The error I get when it fails is "Invalid what in exvval", which I don't understand either! With n=5e3 it worked with 6 variables but not with 7. I wasn't sure the error was caused by number of variables rather than something else, so I tried with... n <- 100 I also tried locfit rather than locfit.raw using... xd <- lapply(1:10, function(x) runif(n)) xd <- as.data.frame(xd) names(xd) <- paste("x", 1:10, sep="") y=rowSums(xd) xd$y <- y aF <- formula(paste("y ~ lp(",paste(names(xd)[1:6], collapse=","), ")")) locfit(aF, xd) Both of these gave the same results, success with 6 variables but not with 7. IT APPEARS, the maximum number of predictors is 6, but I don't know locfit well, and it may be that other settings would allow more variables. CAN anyone give a more DEFINITIVE ANSWER? My current data sets currently reach 5 predictors, and I expect this it increase. In S-Plus (v6.2.1) I used loess in which "Locally quadratic models may have at most 4 predictor variables; locally linear models may have at most 15". In R stats::loess allows only "one to four numeric predictors". I'd assumed (foolishly) that because locfit didn't mention limits, the only limits were practical (memory, time,...) - it seems not :-( I guess I could write something myself, I only need rough interpolation, even "straight line" interpolation between nearest neighbours would be OK. But at first glance it seems non-trivial with a substantial non-fixed number of dimensions (nnclust::nnfind to identify neighbours??), and I don't want to re-invent wheels. Can anyone suggest an ALTERNATIVE route for INTERPOLATION in 5-10 DIMENSIONS? Best... (apologies for capitals, not shouting, just highlighting key points for those skimming quickly) Keith Jewell "Liaw, Andy" <andy_l...@merck.com> wrote in message news:b10baa7d28d88b45af82813c4a6ffa934ce...@usctmx1157.merck.com... > Well, I should think there's an obvious (if not elegant) way to test it: > > n <- 5e3 > m <- 20 > x <- matrix(runif(n * m), nrow=n) > y <- rnorm(n) > > require(locfit) > fit <- locfit.raw(x[, 1:10], y) > > The code above took a while on my laptop, and ended up giving some error > I don't understand. Not sure if the error was caused by insufficient > sample size, or some inherent limitation. At least it didn't choke on > five variables. However, if all 20 columns of x is used, locfit.raw() > will choke because it can't compute the dimension of some variable that > it needs to allocate memory for. > > I had vague recollection of reading that "5" is the limit somewhere. > Unfortunately my copy of Local Regression and Likelihood has been MIA > for a few years, so I can't check there. In any case it doesn't seem > like the number of data points and/or computing power are bigger issue. > > Andy > >> -----Original Message----- >> From: r-help-boun...@r-project.org >> [mailto:r-help-boun...@r-project.org] On Behalf Of Keith Jewell >> Sent: Thursday, February 25, 2010 4:11 AM >> To: r-h...@stat.math.ethz.ch >> Subject: [R] locfit: max number of predictors? >> >> Hi All, >> >> In another thread Andy Liaw, who CRAN lists as locfit >> maintainer; said: >> <quote> >> From: "Liaw, Andy" <andy_l...@merck.com> >> To: "Guy Green" <guygr...@netvigator.com>; <r-help@r-project.org> >> Subject: Re: Alternatives to linear regression with multiple variables >> Date: 22 February 2010 17:50 >> >> You can try the locfit package, which I believe can handle up to 5 >> variables. E.g., >> </quote> >> >> Looking in the locfit documentation (e.g. >> http://www.stats.bris.ac.uk/R/web/packages/locfit/locfit.pdf) >> I can't see an >> upper limit on the number of predictors; if it is 5 I'm >> getting close in one >> of my applications. >> >> Can anyone confirm or deny the existence of a 'crisp' upper >> limit on the >> number of predictors in locfit? >> >> If it is 5, or thereabouts, can anyone suggest an alternative >> which can >> handle a few more? (I'm using it for multidimensional interpolation). >> >> Best regards, >> >> Keith Jewell >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > Notice: This e-mail message, together with any attachme...{{dropped:10}} > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.