Thank you for the reply, it looks like the second option (te) will work perfectly!
Max On Tue, Sep 20, 2011 at 2:39 PM, Max Farrell <ma...@umich.edu> wrote: > One possibility is.... > > library(mgcv) > > ## isotropic thin plate spline smoother > b <- gam(Y~s(X[,1],X[,2])) > predict(b,newdata=list(X=W)) > > ## tensor product smoother > b <- gam(Y~te(X[,1],X[,2])) > predict(b,newdata=list(X=W)) > > ## variant tensor product smoother > b <- gam(Y~t2(X[,1],X[,2])) > predict(b,newdata=list(X=W)) > > ... these would all result in penalized regression spline fits with > smoothing parameters estimated (by GCV, by default). If you don't want > penalization then use, e.g. s(X[,1],X[,2],fx=TRUE) to get pure > regression spline (`k' argument to s, te and t2 controls spline basis > dimension --- see docs). > > best, > simon > > On 09/20/2011 03:11 PM, Max Farrell wrote: >> Hello, >> >> I am trying to estimate a multivariate regression of Y on X with >> regression splines. Y is (nx1), and X is (nxd), with d>1. I assume the >> data is generated by some unknown regression function f(X), as in Y = >> f(X) + u, where u is some well-behaved regression error. I want to >> estimate f(X) via regression splines (tensor product splines). Then, I >> want to get the predicted values for some new points W. >> >> To be concrete, here is an example of what I want: >> >> #dimensions of the model >> d=2 >> n=1000 >> #some random data >> X<- matrix(runif(d*n,-2,2),n,d) >> U<- rnorm(n) >> Y<- X[,1] + X[,2] + U >> # a new point for prediction >> W<- matrix(rep(0),1,d) >> >> Now if I wanted to use local polynomials instead of splines, I could >> load the 'locfit' package and run (something like): >> >> lp.results<- >> smooth.lf(X,Y,kern="epan",kt="prod",deg=1,alpha=c(0,0.25,0),xev=W,direct=TRUE)$y >> >> Or, if X was univariate (ie d=1), I could use (something like): >> >> spl.results<- predict(smooth.spline(X,Y, nknots=6),W) >> >> But smooth.spline only works for univariate data. I looked at the >> "crs" package, and it at least will fit the multivariate spline, but I >> don't see how to predict the new data from this. That is, I run a >> command like: >> >> spl.fit<- crs(Y~X[,1] + X[,2],basis="tensor", >> degree=c(3,3),segments=c(4,4),degree.min=3,degree.max=3, kernel=FALSE, >> cv="none",knots="uniform",prune=FALSE) >> >> Then what? >> >> What I really want is the spline version of the smooth.lf command >> above, or the multivariate version of smooth.spline. Any ideas/help? >> >> Thanks, >> Max >> >> ______________________________________________ >> R-help at 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. >> > ______________________________________________ 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.
