similar to: Translation of R code required

From: Martin Maechler <maechler at stat.math.ethz.ch> MIME-Version: 1.0 Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: 7bit Message-ID: <17739.46076.735981.117358 at stat.math.ethz.ch> Date: Fri, 3 Nov 2006 22:26:20 +0100 To: Barry Rowlingson <B.Rowlingson at lancaster.ac.uk> Cc: Sarah Goslee <sarah.goslee at gmail.com>, r-help at

From: Donovan R. Palmer [mailto:palmerd@nildram.co.uk] Sent: 22 May 2000 10:42 To: Multiple recipients of list SAMBA Subject: Off Topic - Virus Hello, I know this is off topic, but I've come to the conclusion that the only people who can answer my question is other Samba users... I would like to deploy on our network a centrally administered antivirus program. We're running Win98

Hi, I would like to suggest a modification to the SWAT web gui. I have been successfully using Samba for a few years now at my institution and it is a great product. We use it here to handle all of our file serving for around 11,000 users. We have 3 Sun E450's serving up to a 1000 clients each at any one time with no problems. That's the background, here is my request. In the SWAT gui

Andrew, Did anyone reply to you on this? By the sounds of things you are running a pair of E450's? We currently use Samba on about 6 or 7 Sun E450's and E250's, it scales very well. I have managed to have about 700-800 current clients connected from Windows 95 and 98, we also have the about 30-40 NT clients that connect regularly. I have found that Samba has scaled uniformly in

Date: Fri, 31 Mar 2000 10:49:21 +0900 (JST) From: Steve Frampton <frampton@j-com.co.jp> To: Multiple recipients of list SAMBA <samba@samba.org> Subject: PC Virus scanning on *nix side? Message-ID: <Pine.LNX.4.21.0003311046390.7302-100000@firewall.j-com.co.jp> MIME-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII -----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi:

Dear R users, I have been using the pdMat class "pdIdnot" (from the mgcv package)instead of "pdIdent" to avoid overflow in GLMM fits with the MASS package function glmmPQL, of the following form: fit1 <- glmmPQL(fixed=y0~-1+xx0, random=list(gp=pdIdent(~-1+zz0)), family=binomial) # vulnerable to overflow fit2 <- glmmPQL(fixed=y0~-1+xx0,

I asked a few days ago about the difference in results I saw between the MASS function glmmPQL (due to Venables and Ripley) and the lme function from the package nlme (due to Pinheiro and Bates). When the two tools apply to the same model (gaussian, link=identity, correlation=AR1), I was getting different results and wondered if there was an argument in favor of one or the other. Several

Dear R-ers, I am having trouble understanding why I am getting an error using glmmPQL (library MASS). I am getting the following error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 The long story: I have data from an experiment on pairwise comparisons between 3 treatments (a, b, c). So a typical run of an experiment

Please excuse me for having posted a similar question on ecolog, but thus far I have received few useful answers there. I am looking for some advice concerning techniques in R that are appropriate for correlated count data. Specifically, I have some "freezing days" data, which is a count of the number of days each spring that were below freezing. The counts were taken at the same

[I think I've seen this reported before but can't locate it any more. I believe this oddity (glitch? feature?) is behind a query that Jean-Baptiste Ferdy asked a year ago <http://finzi.psych.upenn.edu/Rhelp08/2008-October/176449.html>] It appears that glmmPQL looks in the global workspace, not within the data frame specified by the "data" argument, for the variables

I have come across the previous communication on this list in September (copied below) because I had received the same error message. I understand from Brian Ripley's reply that anova should not be used with glmmPQL because it is not an adequate method, and that this is now shown with an error message. My question is, what method *should* be used? Using summary does not give me the result

I'm a bit puzzled by how to write out additive random effects in glmmPQL. In my situation, I have a factorial design on two (categorical) random factors, A and B. At each combination, I have a binary response, y, and two binary fixed covariates, C and D. If everything were fixed, I would use glm(y ~ A + B + C + D, family = binomial) My first thought was to use glmmPQL(y ~ A + B, random

Dear R Community, I?m relatively new in the field of R and I hope someone of you can help me to solve my nerv-racking problem. For my Master thesis I collected some behavioral data of fish using acoustic telemetry. The aim of the study is to compare two different groups of fish (coded as 0 and 1 which should be the dependent variable) based on their swimming activity, habitat choice, etc.

Hi ! I try to compare some models obtained from glmmPQL. model1 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 +I(freq8_4^2), random=~1|num, binomial); model2 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 , random=~1|num, binomial); anova(model1, model2) here is the answer : Erreur dans anova.lme(model1, model2) : Objects must

I fit the following model using glmmPQL from MASS: fit.glmmPQL <- glmmPQL(ifelse(class=="Disease",1,0)~age+x1+x2,random=~1|subject,family=binomial) summary(fit.glmmPQL) The response is paired (pairing denoted by subject), although some subjects only have one response. Also, there is a perfect positive correlation between the paired responses. x1 and x2 can and do differ within each

Hello All, I have a question regarding how glmmPQL should be specified. Which of these two is correct? summary(fm.3 <- glmmPQL(cbind(response, 100 - response) ~ expt, data = data.1, random = ~ 1 | subject, family = binomial)) summary(fm.4 <- glmmPQL(response ~ expt, data = data.2, random = ~ 1 | subject, family =

Dear R HELP, ABOUT glmmPQL and the anova command. Here is an example of a repeated-measures ANOVA focussing on the way starling masses vary according to (i) roost situation and (ii) time (two time points only). library(nlme);library(MASS)

Hello, I am using the R function glmmPQL to fit a logistic GLMM, with weights. I am finding that I get fairly different parameter estimates in glmmPQL from fitting the full dataset (with no "weight" statement) and an equivalent, shorter dataset with the weights statement. I am using the weights statement in the 'glmmPQL' function exactly as in the 'glm' function. I

Hi all, I try to make a split-plot with poisson errors using glmmPQL, but I have some doubts about the model simplification. Look my system: Block = 3 blocks Xvar1 = 2 levels Xvar2 = 13 levels Yvar = Count data Response I need know about the behaviour of Var1, Var2 and interaction Var1:Var2. Look the levels: > levels(Xvar1) [1] "A" "B" > levels(Xvar2) [1]

Dear all, I want to evaluate several generalized linear mixed models, including the null model, and select the best approximating one. I have tried glmmPQL (MASS library) and GLMM (lme4) to fit the models. Both result in similar parameter estimates but fairly different likelihood estimates. My questions: 1- Is it correct to calculate AIC for comparing my models, given that they use