You can try the locfit package, which I believe can handle up to 5 variables. E.g.,
R> library(locfit) Loading required package: akima Loading required package: lattice locfit 1.5-6 2010-01-20 R> x <- matrix(runif(1000 * 3), 1000, 3) R> y <- rnorm(1000) R> mydata <- data.frame(x, y) R> str(mydata) 'data.frame': 1000 obs. of 4 variables: $ X1: num 0.21 0.769 0.661 0.978 0.15 ... $ X2: num 0.426 0.132 0.214 0.774 0.472 ... $ X3: num 0.971 0.659 0.474 0.867 0.479 ... $ y : num -0.496 -0.636 1.778 -0.876 0.657 ... R> fit <- locfit(y ~ lf(X1, X2, X3), data=mydata) R> plot(fit) Andy > -----Original Message----- > From: r-help-boun...@r-project.org > [mailto:r-help-boun...@r-project.org] On Behalf Of Guy Green > Sent: Monday, February 22, 2010 7:47 AM > To: r-help@r-project.org > Subject: [R] Alternatives to linear regression with multiple variables > > > I wonder if someone can give some pointers on alternatives to linear > regression (e.g. Loess) when dealing with multiple variables. > > Taking any simple table with three variables, you can very > easily get the > intercept and coefficients with: > summary(lm(read_table)) > > For obvious reasons, the coefficients in a multiple > regression are quite > different from what you get if you calculate regressions for > the single > variables separately. Alternative approaches such as Loess seem > straightforward when you have only one variable, and have the > advantage that > they can cope even if the relationship is not linear. > > My question is: how can you extend a flexible approach like Loess to a > multi-variable scenario? I assume that any non-parametric calculation > becomes very resource-intensive very quickly. Can anyone suggest > alternatives (preferably R-based) that cope with multiple > variables, even > when the relationship (linear, etc) is not known in advance? > > Thanks, > > Guy > -- > View this message in context: > http://n4.nabble.com/Alternatives-to-linear-regression-with-mu > ltiple-variables-tp1564370p1564370.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > 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.
