similar to: tweedie and 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

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

Dear Catarina, I prefer to leave the AIC value as NA for the tweedie GLM family because it takes extra time to compute and is only occasionally wanted. It's easy to compute the AIC yourself using the dtweedie() function of the tweedie package. Best wishes Gordon At 03:05 AM 14/08/2007, Catarina Miranda wrote: >Dear Gordon; > >I have also sent this email to R help mailing list,

Dear R-users, I'm trying to model some data using a tweedie GLM approach. My response variable is the number of pupae that are the offspring of a subordinate wasp on a wasp's nest. However, they're not count data- for each nest, I only know the mean number of pupae per subordinate, which is continous. The data also contain a high proportion of zeros. I'm not very experienced at

Hi there, I'm using the tweedie distribution package and I cant figure out how to run a model using an identity link. I know I can use a log link by having link.power=0 and I think identity would be link.power=1, but I'm not sure. Furthermore when I try running it with link.power=1 it requires starting values which I cant manage to give appropriately so I'm not sure if its

Dear R users; I am modelling densities of some species of birds, so I have a problem with a great ammount of zeros. I have decided to try GLMs with the tweedie family, but in all the models I have tried I got an NA for the AIC value. Just to check the problem I've compared the a glm using the Gaussian family with the identity link and a glm using the tweedie family with var.power=0 and

Post-hoc test for a zero inflated continuous data set with a tweedie distribution? I have a zero inflated continuous data set of aphid feeding duration on 10+ species of plant. I have fitted a glm model with a tweedie distribution and used anova() function to show that there is significance between the plant species. However, I would now like to perform of post-hoc test, ideally a Tukey-Kramer

Hi there, I've been wanting to fit a GLMM and I'm not completely sure I'm doing things right. As I said in a previous message my response variable is continuous with many zeros, so I was having a hard time finding an appropriate error distribution. I read some previous help mails given to other people advising them to use the Tweedie distribution. I'm still not sure if this

Hi all I've been getting some emails from the R package maintainers that I need to update some code in a CRAN packge that uses FORTRAN, to comply with (not so recent) changes. I've been a little busy... I'm having trouble adjusting my code. I hope someone can help. The package was working fine, and a few R functions in my package had lines like this: tmp <- .Fortran(

Good afternoon My name is Joanne Lenehan, I am a post grad at UNE using R version 2.9.0 I came across the Tweedie package in old R help posts and was interested in giving it a go for some data. The data is below and also attached as BaregroundLitterLogs Site Treatment Graze Dam Plot Time Bare Litter Logs 1 C remote yes 1 A 0 2 0 1 C remote yes 2 A 0 15 0 1 HE

I am trying to write a wrapper for the last example in help(cv.glm) that deals with leave-one-out-cross-validation (LOOCV) for a logistic model. This wrapper will be used as part of a bigger program. Here is my wrapper funtion : logistic.LOOCV.err <- function( formu=NULL, data=NULL ){ cost.fn <- function(cl, pred) mean( abs(cl-pred) > 0.5 ) glmfit <- glm(

Hello there, I was wondering if somebody could offer me some advice on which error distribution would be appropriate for the type of data I have. I'm studying what continuous predictor variables such as grooming received, rank, etc. affect the amount of grooming given. This response variable is continuous with many zeros, and so positively skewed. So I can't use gamma because of the

Hi R-users: New to R and I am trying to run a GLM with random effects. I have 3 replicates ('Replicate) of counts of parasites ('nor.tot.lep') before and after an experiment ('In.Out'). When I run lmer I get the error messages (16 of each) below... > lmer(nor.tot.lep ~ In.Out + (In.Out|Replicate),data=coho, family =tweedie(var.power = 1, + link.power = 1)) Generalized

Dear all, I've been having problems running update() to re-fit a cpglm model inside a function (as in the code below). The solution is probably simple, but I'm stuck. If anyone could help, I'd greatly appreciate it. Regards, Rubem ## R code library(cplm) ## Data simulation period<-factor(1:4)                        herd<-factor(1:50)  

Hello R-users, First my settings: R-1.8.1, compiled as a 64bit application for a Solaris 5.8, 64 bit. The OS has 8Gb of RAM available and I am the sole user of the machine, hence pretty much all the 8Gb are available to R. I am pretty new to R and I am having a hard time to work with large data sets, which make up over 90% of the anlyses done here. The data set I imported in R, from S+, has a

Hi Dr. Snow, I am a graduate student working on analyzing data for my thesis and came across your post on an R forum: The default link function for the glm poisson family is a log link, which means that it is fitting the model: log(mu) ~ b0 + b1 * x But the data that you generate is based on a linear link. Therefore your glm analysis does not match with how the data was generated

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"

If a Xen guest has a bootloader configured, then it will fail to start on a blktap image. The problem is blkdev_uname_to_file, which cannot parse the "tap:aio:$filename" image strings: it tries to split the string apart at ":" and assign the result to a 2-tuple, and this results in a python error if the split results in three or more strings. The fix is to split only at the

I read the past archives by Stephen Tweedie regarding this error in 7.2 and in kernels prior to the 2.4.18-4 errata update. Since this is the only error I am experiencing (running 2.4.18-3), and I am feeling too lazy to patch all of our systems, do you feel it is necessary to patch the entire kernel to 2.4.18-4 or above, or is there a patch specific to the ext3 filesystem that could be