search for: supernodes

Hello list, we have been running tinc to connect multiple nodes without problems for quite some time now. Thanks for this great piece of software! Our configuration is as follows: Two "supernodes" A and B running the tinc daemon are publicly reachable from the internet. Node A is running Linux and has a static public IP address. Node B is running Windows with port forwarding through a firewall with a DynDNS address. All our tinc nodes connect to both supernodes. We are running tinc...

Hi, I am getting a warning message when I am fitting a generalized linear mixed model (m1.2 below). CHOLMOD warning: matrix not positive definite Error in objective(.par, ...) : Cholmod error `matrix not positive definite' at file:../Supernodal/t_cholmod_super_numeric.c, line 614 Any idea? Thanks for your help, Reza > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32

Dear R-users, I'm new to R, so my apologies if this question doesn't make sense. I've tried the following model in lmer, and it works perfectly: model<-lmer(aphids~densroot+zone+(1|zone/site), family=quasipoisson) But if I try the exact same model with a different variable, totmas, the model looks as follows: model<-lmer(aphids~totmas+zone+(1|zone/site), family=quasipoisson)

Hi Folks, I think I have searched exhaustively, including, of course R-help (D. Bates, S. Graves, and others) and but I remain uncertain about testing fixed effects with lmer(..., family=binomial). I gather that mcmcsamp does not work with Do we rely exclusively on z values of model parameters, or could we use anova() with likelihood ratios, AIC and BIC, with (or without)

Hello tinc users, I have been trying to work out how key exchange/hosts file sharing in tinc 1.1 works. My topology is straightforward, a "super" always-online node A to which remote nodes B, C, ... (and so on) ConnectTo = A, to discover each other via AutoConnect (that's on by default in tinc 1.1pre17) Only super A has host files with Ed25519 keys for every node on the network.

This is a "heads up" to those using the lmer function for fitting linear mixed models or generalized linear mixed models. In the next few days Martin and I will upload a new version of the Matrix package that employs a new representation of linear mixed models based on the supernodal Cholesky factorization provided by Tim Davis's CHOLMOD library of sparse matrix code. This

Dear all, I ran the following model without any problem previously to the update of lme4: fm2<-lmer(data=NGud,family=poisson, seed~hab*seedtray +(1|site)+(1|site:hab)) I have 25 sites, 2 habitats ("hab") per site, 8 seedtrays per habitat (4 as control, and 4 as treatment), and I'm interested in comparing the number of seed in seedtrays as a function of the treatment and its

Hi there, I've been wanting to fit a GLMM and I'm not completely sure I'm doing things right. As I said in a previous message my response variable is continuous with many zeros, so I was having a hard time finding an appropriate error distribution. I read some previous help mails given to other people advising them to use the Tweedie distribution. I'm still not sure if this

This is a "heads up" to those using the lmer function for fitting linear mixed models or generalized linear mixed models. In the next few days Martin and I will upload a new version of the Matrix package that employs a new representation of linear mixed models based on the supernodal Cholesky factorization provided by Tim Davis's CHOLMOD library of sparse matrix code. This

Hi! I get the following message trying to install the matrix pakcage, can anyone help me please? trying URL `http://cran.r-project.org/bin/windows/contrib/2.0/Matrix_0.95-5.zip' Error in download.file(url, destfile, method, mode = "wb") : cannot open URL `http://cran.r-project.org/bin/windows/contrib/2.0/Matrix_0.95-5.zip' In addition: Warning message: cannot open: HTTP

i am using ipp2p module (v 0.6) from http://rnvs.informatik.uni-leipzig.de/ipp2p/index_en.html I have tried it on kazaa and e-donkey connections. Unfortunately, I am interesting in limiting kazaa traffic, which this modules seems not to work right with it. (Kazza traffic is not identified most of the cases....). Has anyone tried this kazza bandwidth control???

Hello, I’m trying to fit the following model: Dependent variable: MAXDEPTH (the maximum depth reached by a penguin during a given dive) Fixed effects: SUCCESSMN (an index of the “individual quality” of a bird), STUDYDAY (the day of the study, from -5 to 20, with 0=Dec 20), and the interaction SUCCESSMN*STUDYDAY Random effect: BIRD (the bird id, as each bird is performing several dives)

I have been using lme from nlme to do a 3-way anova with all the effects treated as random. I was wondering if someone could direct me to an example of how to do this using lmer from lme4. I have 3 main effects, tim, trt, ctr, and all the interaction effects tim*trt*ctr. The response variable is ge. Here is my lme code: dat <-

Hi, this is not a new doubt, but is a doubt that I cant find a good response. Look this output: > m.lme <- lme(Yvar~Xvar,random=~1|Plot1/Plot2/Plot3) > anova(m.lme) numDF denDF F-value p-value (Intercept) 1 860 210.2457 <.0001 Xvar 1 2 1.2352 0.3821 > summary(m.lme) Linear mixed-effects model fit by REML Data: NULL AIC BIC

Hi all, I've now got a problem with some modified lmer code (function lmer1 pasted at end) - I've made only three changes to the lmer code (marked), and I'm not really looking for comments on this function, but would like to know why execution of the following commands that use it almost invariably (but not quite predictably) leads to the R session terminating. Here's the command

Hi all, Somehow I managed to mess with a RAID array containing an ext3 partition. Parenthesis, if it matters: I disconnected physically a drive while the array was online. Next thing, I lost the right order of the drives in the array. While trying to re-create it, I overwrote the raid superblocks. Luckily, the array was RAID5 degraded, so whenever I re-created it, it didn't go into sync;

Thanks to some help by Doug Bates (and the updated version of the Matrix package), I've refined my question about fitting nested and non-nested factors in lmer(). I can get it to work in linear regression but it crashes in logistic regression. Here's my example: # set up the predictors n.age <- 4 n.edu <- 4 n.rep <- 100 n.state <- 50 n <- n.age*n.edu*n.rep age.id

This concerns whether p-values from lmer can be trusted. From simulations, it seems that lmer can produce very small, and probably spurious, p-values. I realize that lmer is not yet a finished product. Is it likely that the problem will be fixed in a future release of the lme4 package? Using simulated data for a quite standard mixed-model anova (a balanced two-way design; see code for the