similar to: What happen for Negative binomial link in Lmer

Dear listers, One of my former students is trying to fit a model of the negative binomial family with lmer. In the past (two years ago), the following call was working well: m1a<-lmer(mapos~ninter+saison+milieu*zone+(1|code),family=neg.bin(0.451),REML=TRUE,data=manu) But now (R version 2.9.2 and lme4 version 0.999375-32), that gives (even with the library MASS loaded):

Dear R users, I'm performing some GLMMs analysis with a negative binomial link. I already performed such analysis some months ago with the lmer() function but when I tried it today I encountered this problem: Erreur dans famType(glmFit$family) : unknown GLM family: 'Negative Binomial' Does anyone know if the negative binomial family has been removed from this function? I really

Context: I have two or more rgl-based views of a given data set, perhaps fitting different models, or showing different things across views. I want to be able to hand-rotate, zoom, scale one view to something I like, and then show the other views with matching viewpoints and scaling. so that one could flip back/forth among graphs and see only the relevant differences. In 2D, all this usually

Sorry I had typo in previous email, this typo corrected version: Dear R experts I have simple question, please execuse me: #example data, the real data consists of 20000 pairs of variables K1 <- c(1,2,1, 1, 1,1); K2 <- c(1, 1,2,2, 1,2); K3 <- c(3, 1, 3, 3, 1, 3) M1a <- rep( K1, 100); M1b <- rep(K2, 100) M2a <- rep(K1, 100); M2b <- rep(K1, 100) M3a <- rep(K1, 100); M3b

Dear R users, I’m performing some GLMMs analysis with a negative binomial link. I already performed such analysis some months ago with the lmer() function but when I tried it today I encountered this problem: *Erreur dans famType(glmFit$family) : unknown GLM family: ‘Negative Binomial’* Does anyone know if the negative binomial family has been removed from this function? I really appreciate any

Hi, I’m sorry, I know that it is a recurrent question but I have not been able to find the response in the Rhelp archives. I think my data require the use of the glmmPQL function but I do not know how to make the model selection. Since the AIC and log-likelihood are apparently meaningless, how can we select the parameters for a model and compare the models to find which one fits best the data?

I have a backup server (Linux Debian 3.1) that connects to several Windows 2000 and 2003 servers shares with smbmount 3.0.14a. This is how I mount the shares: smbmount "\\\\win2000\\inetpubd" /mnt/win2000/ -o username=backups,password=mypwd If I list items with accents, this is a sample of how they appear on the mounted share: drwxr-xr-x 1 root root 4096 2005-12-01 16:45 D?but de

Dear R experts. I might be missing something obvious. I have been trying to fix this problem for some weeks. Please help. #data ped <- c(rep(1, 4), rep(2, 3), rep(3, 3)) y <- rnorm(10, 8, 2) # variable set 1 M1a <- sample (c(1, 2,3), 10, replace= T) M1b <- sample (c(1, 2,3), 10, replace= T) M1aP1 <- sample (c(1, 2,3), 10, replace= T) M1bP2 <- sample (c(1, 2,3), 10, replace= T)

Hello list, Is there any mean to fit models for ordinal response other than multinomial polytomous ("multinom" from nnet ) and cumulative logit ("polr" from MASS)? I am particularly interested in continuation-ratio model and adjacent-category logit model. It is for the sake of epidemiology in wild-living populations! Many thanks, Emmanuelle Fromont

Dear all, It looks like the parameters var-cov matrix returned by gee() is not adjusted for the scale parameter: > fm1 <- gee(nbtrp ~ strate * saison + offset(log(surf)), family = poisson, data = Eff2001, + id = loc, tol = 1e-10, corstr = "exchangeable") [1] "Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27" [1] "running glm to get initial

I too was curious about this, so I copied the text into Babel Fish, and this is the result: I miss of the 2/04/2007 to the 11/04/2007. I will answer your message as of my return. For any urgency, to contact Emmanuelle Parache Moga or C?dric Buzay. If this guy is really going to be out until November these messages will get rather tiresome... John Beaman Telecom Specialist Voice

Hi all, I have two time series that I would like to correlate but as they are autocorrelated, I am "pre-whitening" them first by fitting ARIMA models, then correlating their residuals....as described in https://onlinecourses.science.psu.edu/stat510/?q=node/75 However, http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm discusses some issues with ARIMA in R. In particular, for issue 2, if

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

saemix implements the SAEM (stochastic approximation EM) algorithm for parameter estimation in non-linear mixed effect models, used to model longitudinal data. Longitudinal data are particularly prominent in pharmacokinetics (study of drug concentrations versus time) and pharmacodynamics (study of drug effect versus time), but the SAEM algorithm has also been successfully applied in many

saemix implements the SAEM (stochastic approximation EM) algorithm for parameter estimation in non-linear mixed effect models, used to model longitudinal data. Longitudinal data are particularly prominent in pharmacokinetics (study of drug concentrations versus time) and pharmacodynamics (study of drug effect versus time), but the SAEM algorithm has also been successfully applied in many

I have used library Design (Frank Harrell) in the past but when I tried this week, I get : > library(Design) Error in eval(expr, envir, enclos) : Object "formula.default" not found I updated to the latest version of the library (2.0) with the same result. My version of R may not be the latest but it's recent (and I probably changed since using Design for the last time) :

Hi ! We study the effect of several variables on fruit set for 44 individuals (plants). For each individual, we have the number of fruits, the number of flowers and a value for each variable. Here is our first model in R : y <- cbind(indnbfruits,indnbflowers); model1 <-glm(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4+I (freq8_4^2), quasibinomial); - We have

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645362 I am estimating a system of seemingly unrelated regressions (SUR) in R. Each of the equations has one unique regressor and one common regressor. I am using `gmm::sysGmm` and am experimenting with different weighting matrices. I get the same results (point estimates, standard errors and

Generally speaking, this sort of detailed statistical question about a speccial package in R does not get a reply on this general R programming help list. Instead, I suggest you either email the maintainer (found by ?maintainer) or ask a question on a relevant R task view, such as https://cran.r-project.org/web/views/Econometrics.html . (or any other that you judge to be more appropriate).

Je suis absent du 2/04/2007 au 11/04/2007. Je r?pondrai ? votre message d?s mon retour. Pour toute urgence, contacter Emmanuelle Parache Moga ou C?dric Buzay.