similar to: R-equivalent Stata command: poisson or quasipoisson?

Hi, I have count data that have been meddled with enough to make them non integers. Using glm(poisson) returns a "non integer" error but glm(quasipoisson) does not. Just wondering if anyone knows if I am violating the assumptions of a quasipoisson error structure by using these non-integer response data? Thanks! I'd welcome your thoughts and/or references... Mark

Dear all, I am analyzing counts of seabirds made from line transects at sea. I have been fitting Poisson and negative binomial distributions to the data using the goodfit function from the vcd library. I would also like to evaluate how well a quasi-poisson distribution fits the data. However, none of the potentially suitable functions I have identified (goodfit(vcd), fitdistr(MASS),

Dear list, I have recently encountered an odd error when running glm(dep~indep, quasipoisson): while, with a subset of my data, I could get a perfectly reasonable model, once I include all of my data (17K+ observations, 29 variables), I get the following error: Error in if (any(y < 0)) stop("negative values not allowed for the quasiPoisson family") : missing value where

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

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, Got my issues sorted. Error message solved: I heard from the guy who developed MuMIn and his suggestion worked. "As for the error you get, it seems you are running an old version of MuMIn. Please update the package first." I did (I was only 1 version behind in both R and in MuMIn) and error disappeared! Running quasipoisson GAM in MuMIn: As for my questions on GAM and " to

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

Hi, I would like to obtain the dispersion parameter for a quasipoisson model for later use in calculating QAIC values for model comparison.Can anyone suggest a method of how to go about doing this? The idea I have now is that I could use the residual deviance divided by the residual degrees of freedom to obtain the dispersion parameter. The residual deviance is available in the summary

Hello, I'm having an error when trying to fit the next GLM: >>model<-glm(response ~ CLONE_M + CLONE_F + HATCHING +(CLONE_M*CLONE_F) + (CLONE_M*HATCHING) + (CLONE_F*HATCHING) + (CLONE_M*CLONE_F*HATCHING), family=quasipoisson) >> anova(model, test="Chi") >Error in if (dispersion == 1) Inf else object$df.residual : missing value where TRUE/FALSE needed If I fit

Hi, Please can someone tell me if this is correct for significance tests on count data; 1: If overdispersed, use quasipoisson, drop1(model, test="Chi"), 2: If not overdispersed, use (poisson or quasipoisson), drop1(model, test="Chi"). Thanks for your time, Martin. Martin Hoyle, School of Life and Environmental Sciences, University of Nottingham, University Park, Nottingham,

Dear R-helpers: I have a question related to fitting overdispersed count data using lmer. Basically, I simulate an overdispsed data set by adding an observation-level normal random shock into exp(....+rnorm()). Then I fit a lmer quasipoisson model. The estimation results are very off (see model output of fit.lmer.over.quasi below). Can someone kindly explain to me what went wrong? Many thanks in

Hey Buddy, Hope you have been doing well since last contact. If you have the answer to the following question, please let me know. If you have chance to travel up north. let me know. best, -Sean ---------- Forwarded message ---------- From: Sean Zhang <seanecon@gmail.com> Date: Sat, Apr 11, 2009 at 12:12 PM Subject: question related to fitting overdispersion count data using lmer

Hi Greg, For quasi families I've used extended quasi-likelihood (see Mccullagh and Nelder, Generalized Linear Models 2nd ed, section 9.6) in place of the likelihood/quasi-likelihood in the expression for the (RE)ML score. I hadn't realised that this was possible before the paper was published. best, Simon ps. sorry for slow reply, the original message slipped through my filter for

R 2.7 Windows XP I have two model that have been run using exactly the same data, both fit using glm(). One model is a linear regression (gaussian(link = "identity")) the other a quasipoisson(link = "log"). I have log likelihoods from each model. Is there any way I can determine which model is a better fit to the data? anova() does not appear to work as the models have the

Hi all, Is there a package or function you would recommend for calculating confidence intervals for regression coefficients in quasipoisson models calculated using glm? The intervals need to be simultaneous across several alternative models. I know this is both a statistics and an R question, but I'm guessing some of you know about these things. Thanks, Eileen

Hello !! II'm trying to test for my fixed effects using an lmer with quasipoisson errors. Since my lmer model is corrected for overdispersion using this kind of errors, I should use during model simplification in my Anovas *F test *and not *Chi square test* to compare two models. So I write: > anova(model,model2,test="F") but R keeps performing a Chi square instead of the F

Dear all, I have a quasipoisson glm for which I need confidence bands in a graphic: gm6 <- glm(num_leaves ~ b_dist_min_new, family = quasipoisson, data = beva) summary(gm6) library('VIM') b_dist_min_new <- as.numeric(prepare(beva$dist_min, scaling="classical", transformation="logarithm")). My first steps for the solution are following: range(b_dist_min_new)

hi I'm puzzled as to the relation between the REML score computed by gam and the formula (4) on p.4 here: http://opus.bath.ac.uk/22707/1/Wood_JRSSB_2011_73_1_3.pdf I'm ok with this for poisson, or for quasipoisson when phi=1. However, when phi differs from 1, I'm stuck. #simulate some data library(mgcv) set.seed(1) x1<-runif(500) x2<-rnorm(500)

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

Hello, I have a question about modelling via glm. I have a dataset (see dput) that looks like as if it where poisson distributed (actually I would appreciate that) but it isnt because mean unequals var. > mean (x) [1] 901.7827 > var (x) [1] 132439.3 Anyway, I tried to model it via poisson and quasipoisson. Actually, just to get an impression how glm works. But I dont know how to