similar to: Discrepancy of Neg. Binomial Estimation in R

Hi, I want to fit a GEE with a negative binomial distribution. I have uesd already a poisson glm and then neg binommial to deal with alot of dispersion. In my neg binomial residuals i have some patterns so i have implemented a GEE, but only with a poisson family as i couldnt with neg binomial. However the residual patterns in fact look worse here. When i try and put neg binomial family it

Hi userRs! I am trying to fit some GLM-poisson and neg.binomial. The neg. Binomial model is to account for over-dispersion. When I fit the poisson model i get: (Dispersion parameter for poisson family taken to be 1) However, if I estimate the dispersion coefficient by means of: sum(residuals(fit,type="pearson")^2)/fit$df.res I obtained 2.4. This is theory means over-dispersion since

https://bugs.freedesktop.org/show_bug.cgi?id=86618 Bug ID: 86618 Summary: [NV96] neg modifiers not working in MIN and MAX operations Product: Mesa Version: git Hardware: Other OS: All Status: NEW Severity: normal Priority: medium Component: Drivers/DRI/nouveau

I am using glm.nb, a ~b*c ( b is categorical and c is continuous). when I run this model I get the warning message: Warning messages: 1: In theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = control$trace > : iteration limit reached 2: In theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = control$trace > : iteration limit reached What does this mean? -- Graduate

Thanks for your message! Actually it works quite well for me too. If I then take the trace of the final result below, I end up with a number made up of both a real and an imaginary part. This does not probably mean much if the trace of the matrix below givens me info about the degrees of freedom of a model... Simona >----Messaggio originale---- >Da: RVaradhan at jhmi.edu >Data:

Hi there, I have relative abundance data for 13 mammal species that I collected at various sites that ranged in road density. I'm trying to determine the effect of road density on animal abundance across body sizes. For most species, I have data that was collected in one year but for a few species I have two years of complete data, and would like to use both. Since I have count data, I'm

Fixes: https://bugs.freedesktop.org/show_bug.cgi?id=86618 Signed-off-by: Ilia Mirkin <imirkin at alum.mit.edu> --- src/gallium/drivers/nouveau/codegen/nv50_ir_emit_nv50.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/gallium/drivers/nouveau/codegen/nv50_ir_emit_nv50.cpp b/src/gallium/drivers/nouveau/codegen/nv50_ir_emit_nv50.cpp index 077eba8..3048f3d 100644 ---

Hi Folks, I've just come across a kind of problem which leads me to wonder how to approach it in R. Basically, each a set of items is subjected to a series of "impacts" until it eventually "fails". The "force" of each impact would depend on covariates X,Y say; but as a result of preceding impacts an item would be expected to have a "cumulative

It works! But Once I have the square root of this matrix, how do I convert it to a real (not imaginary) matrix which has the same property? Is that possible? Best, Simon >----Messaggio originale---- >Da: p.dalgaard at biostat.ku.dk >Data: 21-nov-2009 18.56 >A: "Charles C. Berry"<cberry at tajo.ucsd.edu> >Cc: "simona.racioppi at

Dear Peter, thank you very much for your answer. My problem is that I need to calculate the following quantity: solve(chol(A)%*%Y) Y is a 3*3 diagonal matrix and A is a 3*3 matrix. Unfortunately one eigenvalue of A is negative. I can anyway take the square root of A but when I multiply it by Y, the imaginary part of the square root of A is dropped, and I do not get the right answer. I tried

Edward, you also can use the package aod on CRAN, see the help page of the function negbin. Best Matthieu An example: > library(aod) > data(dja) > negbin(y ~ group + offset(log(trisk)), ~group, dja, fixpar = list(4, 0)) Negative-binomial model ----------------------- negbin(formula = y ~ group + offset(log(trisk)), random = ~group, data = dja, fixpar = list(4, 0))

Dear All, I'm analysing a negative binomial dataset from a population-based study. Many covariates were determined on household level, so all members of a household have the same value for those covariates. In STATA, there seems to be an option for 'clustered analysis' for neg-bin regression. Does an equivalent exist for R(MASS)'s glm.nb or a comparable function? Many thanks for

Dear R users, When running the program below I receive the following error message: fit <- optim(parm, objective, yt = tyield, hessian = TRUE) Error in as.vector(data) : no method for coercing this S4 class to a vector I can't figure out what the problem is exactly. I imagine that it has something to do with "tyield" being a matrix. Any help on explaining what's going on

I am analysing parasite egg count data and am having trouble with glm with a negative binomial family. In my first data set, 55% of the 3000 cases have a zero count, and the non-zero counts range from 94 to 145,781. Eventually, I want to run bic.glm, so I need to be able to use glm(family= neg.bin(theta)). But first I ran glm.nb to get an estimate of theta: > hook.nb<- glm.nb(fh,

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):

Hi, I'm trying to estimate the parameters of a state space model of the following form measurement eq: z_t = a + b*y_t + eps_t transition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal

Hi I'm fitting poisson and negative binomial distributions to event data. I'm interested in the expected number of events occuring in a time period. For the poisson this is determined by the parameter lambda only. For the neg. binomial, is the expected number of events determined by the parameter "mu" only or does parameter "size" influence the first moment as

A while ago (April 2002) there was a short thread on software for generalized linear mixed models. Since that time, has anyone written or found R code to use a glmm to analyze binomial data? I study CWD in white-tailed deer, and I'd like to do a similar analysis as Kleinschmidt et al. (2001, Am. J. Epidemiology 153: 1213-1221) used to assess control for spatial structure in malaria

Hi, Using indicator variables I have been able to fit and run the code for fitting a bivariate mixed model using unstructured covariance matrix The code is lme.fit1<- lme(one.var~-1+indic1+indic2+I(indic1*d.time)+I(indic2*d.time), random =~ -1+indic1+indic2|m.unit, weights = varIdent(~1|indic1) ,data = new.data) My variables are one.var :- the two response variables stacked one after

Hello all (especially MCLUS users). I'm trying to make use of the MCLUST package by C. Fraley and A. Raftery. My problem is trying to figure out how the (model) identifier (e.g, EII, VII, VVI, etc.) relates to the covariance matrix. The parameterization of the covariance matrix makes use of the method of decomposition in Banfield and Rraftery (1993) and Fraley and Raftery (2002) where