On Sat, Mar 28, 2009 at 11:47 AM, Peter Dalgaard <p.dalga...@biostat.ku.dk> wrote: > Douglas Bates wrote: > >> >> You could use the LAPACK = TRUE argument to R's qr function to get the >> unconstrained pivoting scheme and use that to get coefficient >> estimates according to the estimated rank of the model matrix (see >> example(qr)) but that won't give you the information needed for the >> analysis of variance decompositions. > > Yup. However, my gut feeling is that there could be a way out: > > First, how important are (sequential) ANOVA decompositions anyway; and > secondly, is it crucial that they can be read directly off the QR > decomposition? There's a lot of code that assumes that this is the case, so > you can't _easily_ plug in a pivoting QR, but the ANOVA can obviously be > obtained by other means - basically just fit the relevant sequence of models > and look at the SSD differences (as I suppose glm() must already do for > deviance tables).
I had proposed another scheme which is to do the unpivoted QR decomposition and check for rank deficiency. If the model matrix is judged to have full column rank then return. Otherwise check the diagonal elements of R for the first apparent singularity, pivot that column to the end, and either update the current decomposition or recalculate the decomposition then iterate. John Chambers said that he might have a student work on something like this for a course project in the Statistical Computing course he is teaching at Stanford. ______________________________________________ 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.
