It has bothered me for quite some time that a smoothing spline fit doesn't allow access to residuals or fitted values in general, since after fit <- smooth.spline(x,y, *)
the resulting fit$x is really equal to the unique (up to 1e-6 precision) sorted original x values and fit$yin (and $y) is accordingly. There are several possible ways to implement the missing feature. My current implementation would add a new argument 'keep.data' which when set to TRUE would make sure that the original (x, y, w) are kept such that fitted values and (weighted or unweighted) residuals are sensibly available from the result. My main RFC (:= request for comments) is about the acceptance of the new behavior to become the *default* (i.e. 'keep.data = TRUE' would be default) such that by default residuals(smooth.spline(...)) will work. The drawback of the new default behavior would be that potentially a 'fit' can become quite a bit larger than previously, e.g. in the following extremely artificial example x0 <- seq(0,1, by = 0.1) x <- sort(sample(x0, 1000, replace = TRUE)) ff <- function(x) 10*(x-1/4)^2 + sin(7*pi*x) y <- ff(x) + rnorm(x) / 2 fit <- smooth.spline(x,y) but typically the size increase will only be less than about 40%. Comments are welcome. Martin Maechler, ETH Zurich ______________________________________________ R-devel@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
