On Tue, 2005-03-01 at 20:30 +0000, Laura Quinn wrote: > Hi, > > Is it possible to recreate "smoothed" data sets in R, by performing a PCA > and then reconstructing a data set from say the first 2/3 EOFs? > > I've had a look in the help pages and don't seem to find anything > relevant. > It's not in the R help, but in the books about PCA in help references.
This can be done, not quite directly. Most of the hassle comes from the centring, and I guess in your case, from scaling of the results. I guess it is best to first scale the results like PCA would do, then make the low-rank approximation, and then de-scale: x <- scale(x, scale = TRUE) pc <- prcomp(x) Full rank will be: xfull <- pc$x %*% pc$rotation The eigenvalues already are incorporated in pc$x, and you don't have to care about them. Then rank=3 approximation will be: x3 <- pc$x[,1:3] %*% pc$rotation[,1:3] Then you have to "de-scale": x3 <- sweep(x3, 2, attr(x, "scaled:scale", "*") x3 <- sweep(x3, 2, attr(x, "scaled:center", "+") And here you are. I wouldn't call this a smoothing, though. Library 'vegan' can do this automatically for PCA run with function 'rda', but there the scaling of raw results is non-conventional (though "biplot"). cheers, jari oksanen -- Jari Oksanen <[EMAIL PROTECTED]> ______________________________________________ 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