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Dear list, Can anyone tell me how to obtain the rank of a sparse Matrix, for example from package Matrix (class dgCMatrix)? Here is an example of QR decomposition of a sparse matrix (from the sparseQR class help). library(Matrix) data(KNex) mm <- KNex$mm str(mmQR <- qr(mm)) Similarly, using the functions/classes from the relatively new MatrixModels package: library(MatrixModels)

Dear list, I am trying to plot a radial dendrogram using the ape package, which requires my data to be of class 'phylo'. Currently I have my dendrogram stored as an object of class 'dendrogram' which was produced from an outside bit of C code, but was made into an object of class 'igraph.eigenc' and converted to a dendrogram using 'as.dendrogram()' from the igraph

Dear list, I have been comparing the outputs of two packages for latent class regression, namely 'flexmix', and 'mmlcr'. What I have noticed is that the flexmix package appears to come up with a much better fit than the mmlcr package (based on logLik, AIC, BIC, and visual inspection). Has anyone else observed such behaviour? Has anyone else been successful in using the mmlcr

Dear list, I'm wondering if anyone can help me calculate the deviance of either a zeroinfl or hurdle model from package pscl? Even if someone could point me to the correct formula for calculating the deviance, I could do the rest on my own. I am trying to calculate a pseudo-R-squared measure based on the R^{2}_{DEV} of [1], so I need to be able to calculate the deviance of the full and null

Dear list, I posted essentially this same question to the r-sig-geo mailing list last week with no response :( Unfortunately I am no closer to reaching a solution, so I now post it here (with some clarifications) in the hope that someone following this list might have an answer for me: Has anyone had much experience with spatial interaction (or gravity) models, specifically in the form of

Hello list, Has anyone had any luck creating an M-step driver for negative binomial regression for use with package flexmix? I've had a look here: http://cran.r-project.org/web/packages/flexmix/vignettes/flexmix-intro.pdf as well as poking around in the flexmix source, but I haven't had much luck getting anything to work. I can't figure out how to a) come up with an initial estimate

Dear list, I have been working with the MatrixModels package quite a bit this week, and it is proving to be extremely valuable for my current work (I am working with several factors with many levels, leading to a sparse model matrix). However, as my knowledge of statistical theory leaves much to be desired, there are certain aspects of model evaluation etc that I am having trouble with. Has

Hello list, I am not sure if the terminology that I am using here is widely used, however, I provide an example in the hopes that my problem will become clear. My basic problem is that I am unsure of how to 'constrain' my model estimates to reproduce the aggregate (by factor levels) observed dependent variable for a negative.binomial model. I realize this sounds rather vague, so I provide

We've been trying unsuccessfully to connect our Meridian Option 81C to a TE110P via PRI. We've followed the directions in asterisk-meridian-a1.pdf (link on http://www.voip-info.org/wiki/view/Asterisk+legacy+integration), but it doesn't seem to work on our 81C (even though many, many users report it works very well on Option 11's). Has anyone had any success in getting the above

From: Roberto Sassu <roberto.sassu at huawei.com> One of the major goals of LSM stacking is to run multiple LSMs side by side without interfering with each other. The ultimate decision will depend on individual LSM decision. Several changes need to be made to the LSM infrastructure to be able to support that. This patch set tackles one of them: gives to each LSM the ability to specify one

From: Roberto Sassu <roberto.sassu at huawei.com> One of the major goals of LSM stacking is to run multiple LSMs side by side without interfering with each other. The ultimate decision will depend on individual LSM decision. Several changes need to be made to the LSM infrastructure to be able to support that. This patch set tackles one of them: gives to each LSM the ability to specify one