similar to: about overdispersed poisson model

Dear R users I have been looking for functions that can deal with overdispersed poisson models. Some (one) of the observations are negative. 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. The presence of negatives is not

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 &

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

Hi all, I have the following data: rep1_treat rep2_treat rep1_control rep2_control 2 3 4 5 100 20 98 54 0 1 2 3 23 32 27

Hi, I have to analyse the number of provisioning trips to nestlings according to a number of biological and environmental factors. I was thinking of building a mixed-effects model with species and nestid as random effects, using a Poisson distribution, but the data are overdispersed (variance/mean = 5). I then thought of using a mixed-effects model with negative binomial distribution, but I have

Dear all, I am using gamlss (Package gamlss version 4.0-0, R version 2.10.1, Windows XP Service Pack 3 on a HP EliteBook) to relate bird counts to habit variables. However, most models fail because “the global deviance is increasing” and I am not sure what causes this behaviour. The dataset consists of counts of birds (duck) and 5 habit variables measured in the field (n= 182). The dependent

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

Information on package 'aod' Description: Package: aod Version: 1.1-2 Date: 2005-06-08 Title: Analysis of Overdispersed Data Author: Matthieu Lesnoff <matthieu.lesnoff at cirad.fr> and Renaud Lancelot <renaud.lancelot at cirad.fr> Maintainer: Renaud Lancelot <renaud.lancelot at cirad.fr> Depends: R (>=

Information on package 'aod' Description: Package: aod Version: 1.1-2 Date: 2005-06-08 Title: Analysis of Overdispersed Data Author: Matthieu Lesnoff <matthieu.lesnoff at cirad.fr> and Renaud Lancelot <renaud.lancelot at cirad.fr> Maintainer: Renaud Lancelot <renaud.lancelot at cirad.fr> Depends: R (>=

Hi all, First, a quick thank you for R; it's amazing. I am trying to fit models for a count dataset following the overdispersed logisitic regression approach outlined in Baggerly et al. (BMC Bioinformatics, 5:144; Annotated R code is given at the end of the paper) but R is returning an error with the data below. Any help in understanding or overcoming this obstacle is appreciated.

I am creating habitat selection models for caribou and other species with data collected from GPS collars. In my current situation the radio-collars recorded the locations of 30 caribou every 6 hours. I am then comparing resources used at caribou locations to random locations using logistic regression (standard habitat analysis). The data is therefore highly autocorrelated and this causes Type

dear list! i am running an anlysis on proportion data using binomial (quasibinomial family) error structure. My data comprises of two continuous vars, body size and range size, as well as of feeding guild, nest placement, nest type and foragig strata as factors. I hope to model with these variables the preference of primary forests (#successes) by certain bird species. My code therefore looks

Dear R-users, Earlier this year I posted a message to this list regarding negative binomial mixed models in R. It was suggested that the program I had written should be turned into an R-package. This has now been done, in collaboration with David Fournier and Anders Nielsen. The R-package glmmADMB provides the following GLMM framework: - Negative binomial or Poisson responses. - Zero-inflation

Dear R-help, How can I replicate the beautiful math plot found in the right-hand side of http://www.r-project.org/screenshots/desktop.jpg? I tried the following code but didn't obtain something as beautiful. r <- seq(-10, 10, len=100) y <- cos(r^2)*exp(-r/6) par(pty="s") plot(r,y,type="l") Thanks in advance! Yung-jui Yang [[alternative HTML version deleted]]

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 a nest are not independent, so the response

Hi all, I am looking for a function in R to trim the last two characters of an 8 character string in a vector. For example, I have the codes 37-079-2, 370079-3,37-079-8 and want to trim them to 37-079 by removing the last two characters. Is sub the correct function to use, and if so how can I specify trimming the last 2 characters? I have read the help file, but can't quite figure out how

I would like to add a footnote to this graph but do not see a "footnote" command in the package:lattice documentation. I would like to note the "span=.8" as the footnote. postscript(file= ?C:/Documents and Settings/dsonneborn/My Documents/Slovak/output/pcb_tables/smooth_PCB_lines_four.ps?, bg=?transparent?, onefile=FALSE, pointsize=20,paper=?letter?, horizontal=TRUE,

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

I am attempting to run a glm with a binomial model to analyze proportion data. I have been following Crawley's book closely and am wondering if there is an accepted standard for how much is too much overdispersion? (e.g. change in AIC has an accepted standard of 2). In the example, he fits several models, binomial and quasibinomial and then accepts the quasibinomial. The output for residual

Hi list, I am trying to convert my Data from 1st following format to the second. Any comment? You could copy following in Tinn R Data<- data.frame(location=c("postcode","sector","long/lat","sector"),Grade=c(" h","m","L","h"),value=c(2,3,5,6)) #Question: how can I reshape Data to the following # Grade postcode