search for: overdispersion

Hi all, I have made an overdispersion test for a data set and get the following result Overdispersion test Obs.Var/Theor.Var Statistic p-value poisson data 16.24267 47444.85 0 after deleting the outliers from the data set I get the following result Overdispersion test Obs.Var/Theor.Var Statistic p-value...

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 1...

...ture, and so I ran a test regression on roadway crash data obtained from "Statistical and Econometric Methods for Transportation Data Analysis" by Washington et al (p. 250). I ran the test (glm.nb from library MASS) and got the same output as in the text for all the parameters except the overdispersion parameter; the text lists 0.516, but R gave me an overdispersion parameter output of 1.9365. (I've attached the raw dataset, in Excel .csv format, if anyone wants to try the test. If the attachment does not go through, please email me and I'll forward it on to you.) Has anyone had simila...

How can I eliminate the overdispersion for binary data apart the use of the quasibinomial? help me Eva Iannario ------------------------------------------------------ Passa a Infostrada. ADSL e Telefono senza limiti e senza canone Telecom http://click.libero.it/infostrada11gen07

Hi, there are a lot of messages dealing with overdispersion, but I couldn't find anything about how to test for overdispersion. I applied a GAM with binomial distribution on my presence/absence data, and would like to check for overdispersion. Does anyone know the command? Many thanks, Anna -- View this message in context: http://www.nabble.com/overd...

I have a few questions relating to overdispersion in a sex ratio data set that I am working with (note that I already have an analysis with GLMMs for fixed effects, this is just to estimate dispersion). The response variable is binomial because nestlings can only be male or female. I have samples of 1-5 nestlings from each nest (individuals within...

Hello! I am trying to model data on species abundance (count data) with a poisson error distribution. I have a fixed and a random variables and thus needs a mixed model. I strongly doubt that my model is overdispersed but I don't know how to get the overdispersion parameter in a mixed model. Maybe someone can help me on this point. Secondly, it seems that quasi models cannot be implemented with the function lmer, is there an option? If not, I certainly should go to variable transformation and use a gaussian error distribution, but it is not optimal. Thank yo...

I would like to try a simple parametric bootstrap, but unfortunately (stupidly?) my models are "overdispersed" gams & glms. I'm hoping for a function that generates overdispersed poisson or negative binomial data with a given mean, scale (& shape parameter). The loose definition I'm using is overdispersed poisson produces integer values with variance=const*mean &

...these variables the preference of primary forests (#successes) by certain bird species. My code therefore looks like: y<-cbind(n_forest,n_trials-n_forest) model<-glm(y~range+body+nstrata+ntype+forage+feed,family=quasibinomial(link=logit),data=dat) however plausible the approach may look, overdispersion is prevalent (dispersion estimated at 6.5). I read up on this and learned that in case of multiple factors, not all levels may yield good results with logistic regression (Crawley "The R Book"). I subsequently try to analyse each feeding guild seperately, but to no avail.overdispersio...

Hi, I am performing glm with binomial family and my data show slight overdispersion (HF<1.5). Nevertheless, in order to take into account for this heterogeneity though weak, I use F-test rather than Chi-square (Krackow & Tkadlec, 2001). But surprisingly, outputs of this two tests are exactly similar. What is the reason and how can I scale the output by overdispersion ?? Th...

...ound is the package glmmADMB but I have a hard time understanding the output. Anyone knows of a R package with an output that gives p values? 2- Two people I asked advice to told me that I should use either a mixed-effect model with a Poisson distribution (the random effects will take care of the overdispersion) OR a glm using neg. bin. distribution but not both at the same time, which would be unnecessary. Any advice is welcome! Thank you Marie-Helene Hachey M.Sc. student Universite Laval, Quebec

I am wondering if stepAIC in the MASS library may be used for model selection in an overdispersed Poisson situation. What I thought of doing was to get an estimate of the overdispersion parameter phi from fitting a model with all or most of the available predictors (we have a large number of observations so this should not be problematical) and then use stepAIC with scale = phi. Should this be OK? Murray Jorgensen -- Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Sta...

I am looking for the correct commands to do the following things: 1. I have a binomial logistic regression model and i want to test for overdispersion. 2. If I do indeed have overdispersion i need to then run a quasi-binomial model, but I'm not sure of the command. 3. I can get the residuals of the model, but i need to then apply a shapiro wilk test to test them. Does anyone know the command for this? Any help would be hugely appreciated, T...

Dear All, I am working with linear mixed-effects models using the lme4 package in R. I created a model using the lmer function including some main effects, a three-way interaction and a random effect. Because I work with a binomial and poisson distribution, I want to know whether there is overdispersion in my data or not. Does anybody know how I can retrieve this information from R? Thank you in advance, Eva Fucikova [[alternative HTML version deleted]]

Dear All I recently proposed a simple modification to Wedderburn's 1974 estimate of overdispersion for count and binomial data, which is used in glm for the quasipoisson and quasibinomial families (see the reference below). Although my motivation for the modification arose from considering sparse data, it will be almost identical to Wedderburn's estimate when the data are not sparse. It...

...ally are highly positively correlated (with an average c=0.6). This is because voters sit together and discuss the issue before taking a vote, but also because they share common ideologies. The coefficient is significant; sign correct; fit is good: R-sq.(adj)=0.866. BUT there seems to be a massive overdispersion: Deviance explained=39.3%, Residual deviance: 3874.0 on 102 degrees of freedom. AND the residual-fitted plot shows heteroscedasticity. The overdispersion cannot be remedied by regressing on LOG(index), or by using the quasibinomial family with a scale parameter for the variance. The estimated Disp...

...eros and small numbers). I measure this variable on a control and intervention cohort 5 times a year. When I analyze analyze this varoable at each time point separately and use GLM with family quasi-Poisson (descrete outcome and two binary variables, gender and cohort, are predictors), I observe an overdispersion. When I use GEE with R software, does GEE R package takes care only about over-dispersion regarding the repeated measure design per se, or it takes care about the over-dispersion within the cohort as well which I observe with GLM method when I choose quasipoisson family? If so what options in GEE...

Dear R users I have been looking for functions that can deal with overdispersed poisson models. According to actuarial literature (England & Verall, Stochastic Claims Reserving in General Insurance , Institute of Actiuaries 2002) this can be handled through the use of quasi likelihoods instead of normal likelihoods. However, we see them frequently in this type of data, and we would like to

...k),data=dataset,family=poisson(link=sqrt)) Is this the only way in which I can specify my random effects? I.e. can I specify them as: (1|Block)+(1|Month)? When I run this model, I do not get any residuals in the error term or estimated scale parameters and so do not know how to check if I have overdispersion. Below is the output I obtained. Generalized linear mixed model fit by the Laplace approximation Formula: Count ~ Treatment * Month + (Month | Block) Data: dataset AIC BIC logLik deviance 310.9 338.5 -146.4 292.9 Random effects: Groups Name Variance Std.Dev. Corr Block (Int...

Dear all, I am fitting an nonlinear glm() using optim() by first minimising glm(resp~ var1 + var2, family=binomial, data=data)$deviance where var1= exp(-a1*dist1), and var2= exp(-a2*dist2), where a1 and a2 are parameters and dist1 and dist2 are independent variables. Next, I calculate the value of var1 (and var2) by plugging in the value of al1 (and al2) that minimises deviance, and fit