similar to: glmmPQL model selection

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

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

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

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

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 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) :

Hello, I am using function lmodel2 to calculate RMA regression between life-history traits in ladybirds beetles. It works well but I am not able to plot an RMA regression line on the scatterplot of my data. I am of course unable to plot the confidence intervals. For ordinary least square regression abline(lm(x~y))works well but for RMA regression, abline(lmodel2(x~y))does not do it. If somebody

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

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

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"

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'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

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

Dear all, for a cross-validation I have to use predict.glmmPQL() , where the formula of the corresponding glmmPQL call is not given explicitly, but constructed using as.formula. However, this does not work as expected: x1<-rnorm(100); x2<-rbinom(100,3,0.5); y<-rpois(100,2) mydata<-data.frame(x1,x2,y) library(MASS) # works as expected model1<-glmmPQL(y~x1, ~1 | factor(x2),

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

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

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