similar to: glm.nb() giving strongly different results

Dear all, I would like to ask, if there is a way to make the variance / dispersion parameter $\theta$ (referring to MASS, 4th edition, p. 206) in the function glm.nb dependent on the data, e.g. $1/ \theta = exp(x \beta)$ and to estimate the parameter vector $\beta$ additionally. If this is not possible with glm.nb, is there another function / package which might do that? Thank you very much for

I have fitted the faults.data to glm.nb and to the function negbin from the package aod. The output of both is the following: summary(glm.nb(n~ll, data=faults)) Call: glm.nb(formula = n ~ ll, data = faults, init.theta = 8.667407437, link = log) Deviance Residuals: Min 1Q Median 3Q Max -2.0470 -0.7815 -0.1723 0.4275 2.0896 Coefficients:

Dear R-collegues, I'm getting an error message (Error in round) when summarising a glm.nb model, and when using anova.negbin (in R 1.3.1 for windows): > m.nb <- glm.nb(tax ~ areal) > m.bn Call: glm.nb(formula = tax ~ areal, init.theta = 5.08829537115498, link = log) Coefficients: (Intercept) areal 3.03146 0.03182 Degrees of Freedom: 283 Total (i.e. Null); 282

Dear R users, I am having problems trying to fit quasipoisson and negative binomials glm. My data set contains abundance (counts) of a species under different management regimens. First, I tried to fit a poisson glm: > summary(model.p<-glm(abund~mgmtcat,poisson)) Call: glm(formula = abund ~ mgmtcat, family = poisson) . . . (Dispersion parameter

Dear list, This is a question about overdispersion and the ML estimates of the parameters returned by the glm.poisson.disp (L. Scrucca) and glm.nb (Venables and Ripley) functions. Both appear to assume a negative binomial distribution for the response variable. Paul and Banerjee (1998) developed C(alpha) tests for "interaction and main effects, in an unbalanced two-way layout of counts

Hi, I am trying to find out how to interpret the summary output from a neg bin GLM? I have 3 significant variables and I can see whether they have a positive or negative effect, but I can't work out how to calculate the magnitude of the effect on the mean of the dependent variable. I used a log link function so I think I might have to use the antilogs of the coefficients but I have no idea

Hello, I have a question regarding estimation of the dispersion parameter (theta) for generalized linear models with the negative binomial error structure. As I understand, there are two main methods to fit glm's using the nb error structure in R: glm.nb() or glm() with the negative.binomial(theta) family. Both functions are implemented through the MASS library. Fitting the model using these

Hi All, I am using the glm.nb function from the MASS package (current version) to fit and compare GLMs with negative binomial error distributions. My question is: what is the appropriate method to use in the anova function to compare models? If only one fitted object like m<-glm.nb(number<-p+sal+temp,data=data) is specified in the anova function (anova(m)), a fixed theta is used to

Dear R users, I am trying to fully understand the difference between estimating overdispersion with glm.nb() from MASS compared to glm(..., family = quasipoisson). It seems that (i) the coefficient estimates are different and also (ii) the summary() method for glm.nb suggests that overdispersion is taken to be one: "Dispersion parameter for Negative Binomial(0.9695) family taken to be

I am running 9 negative binomial regressions with count data. The nine models use 9 different dependent variables - items of a clinical screening instrument - and use the same set of 5 predictors. Goal is to find out whether these predictors have differential effects on the items. Due to various reasons, one being that I want to avoid overfitting models, I need to employ identical types of

Full_Name: Ben Cooper Version: 1.5.0 OS: linux Submission from: (NULL) (134.174.187.90) The help page for glm.nb (in MASS package) says that it takes "Any other arguments for the glm() function except family" One such argument is start "starting values for the parameters in the linear predictor." However, when called with starting values glm.nb returns: Error in

Can anyone explain to me why the AIC values are so different when using glm.nb and glm with a negative.binomial family, from the MASS library? I'm using R 1.8.1 with Mac 0S 10.3.4. >library(MASS) > dfr <- data.frame(c=rnbinom(100,size=2,mu=rep(c(10,20,100,1000),rep(25,4))), + f=factor(rep(seq(1,4),rep(25,4)))) > AIC(nb1 <- glm.nb(c~f, data=dfr)) [1] 1047 >

I came to R from GLIM and its glm. My data sets (ecological community data) are severely over-dispersed, and so I was delighted to find out that the MASS library has glm.nb which is an advancement from the GLIM macros I had used (N.E.Breslow, Applied Statistics 33, 38--44; 1984). However, I need to use offset, but that failed. I am not (yet --- hopefully) fluent enough in R to be able to

I am struggling to generate p values for comparisons of levels (post-hoc tests) in a glm with a negative binomial distribution I am trying to compare cell counts on different days as grown on different media (e.g. types of cryogel) so I have 2 explanatory variables (Day and Cryogel), which are both factors, and an over-dispersed count variable (number of cells) as the response. I know that both

How might you fit a generalized linear model (glm) with variance = mu+theta*mu^2 (where mu = mean of the exponential family random variable and theta is a parameter to be estimated)? This appears in Table 2.7 of Fahrmeir and Tutz (2001) Multivariate Statisticial Modeling Based on Generalized Linear Models, 2nd ed. (Springer, p. 60), where they compare "log-linear model fits to

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,

(Perdón, no puse asunto en el post original) Ah, corremos R 3.0.2 y l alibrería MASS 7.3-29 Estimados todos: Os escribo porque tengo un problema que nos está dejando un poco trastornadillos. Probablemente sea uno de esos que los ve un experto a un kilómetro y dice ¡oh! toca aquí. Pero nos trae de cabeza. El caso es que aquí usamos un script para calcular ciertos parámetros relacionados con el

Hello to everyone. I'm fitting a glm.nb model to a count data. I'm using about 8 predictive variables. Once a run the script I do get a result but it tells me that the iteration limit has been reached. So, can i trust the results given by the model? Could it be a multicollinearity problem? Thank you for taking the time to help me. Greetings Lucas. -- View this message in context:

Dear Lister, I am facing a strange problem fitting a GLM of the negative binomial family. Actually, I tried to estimate theta (the scale parameter) through glm.nb from MASS and could get convergence only relaxing the convergence tolerance to 1e-3. With warning messages: glm1<-glm.nb(nbcas~.,data=zonesdb4,control=glm.control(epsilon = 1e-3)) There were 25 warnings (use warnings() to see

Hi -- subject says all. I just want the code for that function, which I guess was in Venables and Ripley as early as 1994. Well, and for any of the sub-functions that glm.nb calls. I can't install the entire MASS library. If the code for just glm.nb (again, don't want to touch the MASS library, last time I tried to install it was a complete nightmare and fiasco) is somewhere on a