similar to: gam negative.binomial

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 fitted a GAM model with Poisson distribution to a data with about 200 observations. I noticed that the plot of the residuals versus fitted values show a trend. Residuals tend to be lower for higher fitted values. Because, I'm dealing with count data, I'm thinking that this might be due to overdispersion. Is there a way to account for overdispersion in any of the packages MGCV or GAM?

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.

[Please keep r-help in the cc: list] I don't quite know how to interpret the difference between specifying effort as an offset vs. as weights; I would have to spend more time thinking about it/working through it than I have available at the moment. I don't know that specifying effort as weights is *wrong*, but I don't know that it's right or what it is doing: if I were

I would like to test in R what regression fits my data best. My dependent variable is a count, and has a lot of zeros. And I would need some help to determine what model and family to use (poisson or quasipoisson, or zero-inflated poisson regression), and how to test the assumptions. 1) Poisson Regression: as far as I understand, the strong assumption is that dependent variable mean = variance.

Hi all, I was wondering if somebody could give me advice regarding the dispersion parameter in GLMM's. I'm a beginner in R and basically in GLMM's. I've ran a GLMM with a poisson family and got really nice results that conform with theory, as well with results that I've obtained previously with other analysis and that others have obtained in similar studies. But the

Hello, I have been using mgcv to run GAM hurdle models, analyzing presence/absence data with GAM logistic regressions, and then analyzing the data conditional on presence (e.g. without samples with no zeros) with GAMs with a negative binomial distribution. It occurs to me that using the negative binomial distribution on data with no zeros is not right, as the negative binomial allows zeros.

Dear list, I would like to fit a negative binomial regression model as described in "Byers AL, Allore H, Gill TM, Peduzzi PN., Application of negative binomial modeling for discrete outcomes: a case study in aging research. J Clin Epidemiol. 2003 Jun;56(6):559-64" to my data in which the response is count data. There are also 10 predictors that are count data, and I have also 3

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:

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

We are using GAM in mgcv (Wood), relatively new users, and wonder if anyone can advise us on a problem we are encountering as we analyze many short time series datasets. For each dataset, we have four models, each with intercept, predictor x (trend), z (treatment), and int (interaction between x and z). Our models are Model 1: gama1.1 <- gam(y~x+z+int, family=quasipoisson) ##no smooths Model

Hi I am running an lmer which works fine with family=poisson mixed.model<-lmer(nobees~spray+dist+flwabund+flwdiv+round+(1|field),family="poisson",method="ML",na.action=na.omit) But it is overdispersed. I tried using family=quasipoisson but get no P values. This didnt worry me too much as i think my data is closer to negative binomial but i cant find any examples of

Sorry about re-posting this, it never went out to the mailing list when I posted this to r-help forum on Nabble and was pending for a few days, now that I am subscribe to the mailing list I hope that this goes out: I've been a viewer of this forum for a while and it has helped out a lot, but this is my first time posting something. I am running glm models for richness and abundances. For

Dear all, I am trying to use the GEE methodology to fit a trend for the number of butterflies observed at several sites. In total, there are 66 sites, and 19 years for which observations might be available. However, only 326 observations are available (instead of 1254). For the time being, I ignore the large number of missing values, and the fact that GEE is only valid under MCAR. When I run the

Hi, I have a GAM quasipoisson that I'd like to run through MuMIn package - dredge - gettop.models - model.avg However, I'm having no luck with script from an example in MuMIn help file. In MuMIn help they advise "include only models with smooth OR linear term (but not both) for each variable". Their example is: # Example with gam models (based on

Dear all, I am fitting a glm to count data using poison errors with the log link. My goal is to test for the significance of model terms by calling the anova function on two nested models following the recommendation in Michael Crawley's guide to Statistical Computing. Without going into too much detail, essentially, I have a small overdispersion problem (errors do not fit the poisson

Seems the message below and the thread have reveived no attention/answer. The output presented is quite tricky. Looks like if lmer (lme4 0.9975-10) has accepted a negative binomial link with reasonable estimates, although it was not designed for... What can one think about result validity ? Best Patrick Message: 34 Date: Thu, 29 Oct 2009 06:51:24 -0700 (PDT) From: "E. Robardet"

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 fellows, I'm trying to extract the AIC statistic from a GLM model with quasipoisson link. The formula I'm referring to is AIC = -2(maximum loglik) + 2df * phi with phi the overdispersion parameter, as reported in: Peng et al., Model choice in time series studies os air pollution and mortality. J R Stat Soc A, 2006; 162: pag 190. Unfortunately, the function logLik

Hello R-help, According to a research article that covers the topic I'm analyzing, in Stata, a Poisson pseudo-maximum-likelihood (PPML) estimation can be obtained with the command poisson depvar_ij ln(indepvar1_ij) ln(indepvar2_ij) ... ln(indepvarN_ij), robust I looked up Stata help for the command, to understand syntax and such: www.stata.com/help.cgi?poisson Which simply says