similar to: anova with models from glmmPQL

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

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?

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

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

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

Seems the message below and the thread have reveived no attention/answer. The output presented is quite tricky. Looks like if lmer (lme4 0.9975-10) has accepted a negative binomial link with reasonable estimates, although it was not designed for... What can one think about result validity ? Best Patrick Message: 34 Date: Thu, 29 Oct 2009 06:51:24 -0700 (PDT) From: "E. Robardet"

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) :

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

Hello All, Specifying 'type = "response"' when using predict() on a model fit using glm(...,family="binomial") returns fitted probabilities. Is it possible to get the same from a model object fit using glmmPQL() ? Thanks in advance, Rob _____________________________________________________ Rob Keefe Lab: (208) 885-5165 M.S. student

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.

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.

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

I fit a random effects linear model to data, and then tried to use it to predict, but I got this error: > predict(lmeObject, newdata, level=0) Error in eval(mCall$fixed)[-2] : object is not subsettable This is a new error for me. It still occurs if I change the level to 1 or if I change the data for prediction back to the original dataset to which the lme model was fitted. Can anyone help?

Ah, yes. One of the many differences between the US and the rest of the world. >>> support@drdos.info 4/3/2007 2:52:16 PM >>> john beaman wrote: > 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

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.

November? It's DD/MM/YYYY in his case, not MM/DD/YYYY. Either way, even two days is more than enough for me. -----Original Message----- From: asterisk-users-bounces@lists.digium.com [mailto:asterisk-users-bounces@lists.digium.com] On Behalf Of john beaman Sent: Tuesday, April 03, 2007 12:43 To: asterisk-users@lists.digium.com Subject: Re: [asterisk-users] Re: asterisk-users Digest, Vol 33,

Hello, I have problems using the functions as.Date and strptime/strftime of base package and some functions of package zoo in my computer (Apple, Mac OS X 10.3.9). When I use them, the R application closes... However, it depends on the length of the vector I want to transform. For example, in the data.frame: climat[c(1:5,1460:1462),] date TEMP_STG PRECIP_STG 1 01/01/01

Dear List, Is there a way to extract variance components from lmeObjects or summary.lme objects without using intervals()? For my purposes I don't need the confidence intervals which I'm obtaining using parametric bootstrap. Thanks, Mike [[alternative HTML version deleted]]

Dear colleague, I'd like to compute linear discriminant analysis, using R. In the book Modern applied statistic with Splus (Venables & Ripley, p. 396), lda function is used. Could you tell me where I can find this function? At what site, can I download this library ? Thank for your help. Best Regards Sovan ---------------------------------------------------------------- Prof. Sovan