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@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.
