similar to: truncated poisson in glm / glmmPQL

Hello As a postgrad I have been helping an undergraduate student with R coding but she has a problem with R studio on her laptop which I can't fix - basically when she runs a plot it appears without a y axis label with the black line plot frame hard against the plot window and the bottom of the plot, where you would expect to see the horizontal axis and the x axis label etc is completely

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 all, I'm running into some computer issues when trying to run a binomial model for spatially correlated data using glmmPQL and was wondering if anyone could help me out. My whole dataset consists of about 300,000 points for which I have a suite of environmental variables (I'm trying to come up with a habitat model for a species of seal, using real (presence) and simulated dives

it is possible to use musiconhold. i added exten => s,5,SetMusicOnHold,default exten => s,7,dial,SIP/michael&SIP/frank|15|m to the extension conf. the logfile looks good, i think -- Called michael -- Called joern -- Started music on hold, class 'default', on CAPI[contr1/9999] -- SIP/192.168.100.150:5060 is ringing -- Stopped music on hold on CAPI[contr1/9999] --

I use model comparison with glms without mixed effects with anova(modelA,modelB), with mixed effects glm (glmmPQL), this doesn't work. Is there a way to compare model fits with glmmPQL's? Paula M. den Hartog Behavioural Biology Institute of Biology Leiden Leiden University [[alternative HTML version deleted]]

Hi all - R2.0.1 on OSX;MASS library;nlme library I am trying to emulate the solution to a problem set that has normally been run in Genstat, using R. The problem that I am having at the moment is with the following glmm question (using glmmPQL from the MASS library): "We have two different forest habitats (first rotation thicket, and high forest) which we want to survey for the

Dear all, I try to fit a glmmPQL on a huge data with 384189 individuals id=1:384189: working in 1520 establishments est:1:1516. The minimum number of individuals in every establishment is 30. This works for a subsample excluding establishemnet cells smaller than 100, but fail when we include smaller cells: R> summary(glmmPQL(count ~ + I( age-ave(age,est) )* ave(age,est) + + I(

(Sorry for the slightly off topic post) I'm giving a talk (on data mining) to some non-statisticians (who're all postgrad students, but a mixture of Science and Commerce majors). My intention is to show them the importance of statistics when doing data mining. What I'm thinking of doing is using, hopefully, two datasets. One from scientific area and another that is

Hey Joern, the Problem is that I installed an Debian package and how can I find out which version was included? But I guess it is the standart version and I will have a look at the C code but it's been a while... But thanks anyway -----Urspr?ngliche Nachricht----- Von: Joern Nettingsmeier [mailto:nettings@folkwang-hochschule.de] Gesendet: Freitag, 15. April 2005 13:17 An: Tobias Grunow Cc:

Hi guys, It would be great if you could help me with this one... I'm looking to create a script to convert a matrix of species abundance e.g: <http://r.789695.n4.nabble.com/file/n4643978/species_matrix.jpg> into two vectors e.g: <http://r.789695.n4.nabble.com/file/n4643978/communitylist.jpg> ---------------- If you feel there is no easy answer to this and that it would be

Hi all, I am analyzing data on habitat utilization of seals in the Southern Ocean. My data show spatial autocorrelation, which I'm interested in incorporating into my model. I am trying to model the presence of dives (versus simulated pseudo-absences) using a binomial generalized binomial model (glmmPQL), since I can incorporate the autocorrelation structure to the model using that package.

Hi, I have the following problem: (I'm not sure if have to send this message to you, or to Microsoft. ;-) Samba 2.2.3a or 2.2.8a (not restarted yet) Network of Windows NT 4.0 PCs and one Windows 2000 Server. The Windows NT Workingsations are working fine, but now I have to integrate an Windows 2000 Server and with this Server I have the following problem. When I open an XXXXX.doc-file

I have R version 2.9.1 on my computer and the anlaysis is not working because I need to update to R version 2.12.0 the latest release. The person incharge of IT tried to download R version 2.12.0 but .exe file referenced in install isn't there - What might we be doing wrong? We have downloaded as tar.gz, uncompress then look for .exe file but not present. Many thanks, Morris

On Wed, 5 Mar 2003, Michael Smith wrote: > On Wednesday 05 March 2003 19:13, Joern Nettingsmeier wrote: > > i'm test-driving an icecast2 relay on a rather crappy network > > connection. to hammer on it, i was listening to the stream with xmms > > while doing a couple of parallel wget -O - http://..mystream 2&>1 > > > > >/dev/null from another

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 listers, glmmPQL (package MASS) is given to work by repeated call to lme. In the classical outputs glmmPQL the Variance Structure is given as " fixed weights, Formula: ~invwt". The script shows that the function varFixed() is used, though the place where 'invwt' is defined remains unclear to me. I wonder if there is an easy way to specify another variance

Dear R-help, I recently tried implementing glmmPQL in 2.3.1, and I discovered a few differences as compared to 2.2.1. I am fitting a regression with fixed and random effects with Gamma error structure. First, 2.3.1 gives different estimates than 2.2.1, and 2.3.1, takes more iterations to converge. Second, when I try using the anova function it says, "'anova' is not available

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

I have a statistical problem which has given me no end of grief recently, and am posting here in the hope that somebody can give me a straight answer. I'm a IT postgrad, not a statistician, so people may have to speak really slowly and clearly for me to get it :) I am collecting simulation data, and the results are geometrically distributed (or approximately so). From what I can gather

Hi, In glmmPQL in the MASS library, the function uses repeated calls to the function lme(), using ML. Does anyone know how you can change this to REML? I know that in lme(), the default is actually set to REML and you can also specify this as 'method=REML' or 'method'ML' but this isn't applicable to glmmPQL(). I'd appreciate any help or advice! Thanks, Emma