similar to: Neg Binomial In GEE

Dear all, I have a puzzle regarding the estimation of Neg. Binomial event count model in R. I would greatly appreciate if anyone could shed some light on my puzzle. Using the glm.nb command, or the zelig command developed by Gary King et. al., I obtain the same point estimates in R as well as in Stata. However, if I write my own likelihood function to estimate a neg. binomial event count

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

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 ---

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

Hi, I am struggling to create a 2 by 2 multiple graphs in one page. I used par(mfrow=c(2,2)) to divide the screen into 4. In each screen I draw a pie chart (They are all same). For example, my data is like this Concentration value A1 69 A2 8 G1 51 G2 1

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

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

dear all, i'm trying to reproduce an spss-anova in R. It is an 2x3x3 repeated measures desingn with repeated contrasts. In R i've coded a contrast matrix for all factors and made a split in the aov summary - but I can't get the repeated interaction contrasts. The output from SPSS looks like this: TaskSw * CongNow * CongBefore: SS df Mean Square F Sig. 1 vs. 2 1 vs. 2 1 vs. 2

Hello, I am interested in running generalized estimating equation models in R. Currently there are two main packages for doing so in R, geepack and gee. I understand that even though one can obtain similar to almost identical results using either of the two, that there are differences between the packages. The paper that introduces the geepack package (

Dear R-help, I was recently wanting to use GEE with the negative binomial "family". It seems that this is lacking in the otherwise excellent implementations of the GEE methodology ( packages: gee, yags, geepack). I would have thought it a simple step to allow the creation of a family, i.e providing the link function (log mu) and the variance function (mu + mu^2/theta) , assuming theta

Hi, having used both the gee pacakge and the geepack package, i am unsure of how to interpret the results. Here are the results from the geeglm function from the geepack package > gee2<-geeglm(data$erythema~data$product, data = data, id=subject, > family=binomial, corstr="independence") Warning message: In model.response(mf, "numeric") : using

Hi, Can someone tell me if it is possible to set the dispersion parameter constant when fitting a negative binomial glm in R? I've looked at the documentation and can't find the appropriate argument to pass. In STATA I can type: nbreg depvar [indepvar...], offset(offset) dispersion(constant). Thank you [[alternative HTML version deleted]]

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 list, I'm currently trying to develop a model to assess clam yield potential in a lagoon. I'm using the zeroinfl function of the pscl package to fit a Zero-inflated negative binomial model, given the high occurrence of zero counts. I don't understand from the sentence in the pscl guide "Zero-inflated count models are a type of two-component mixture model, with a component