similar to: Dataset Quasi Poisson

Hi list, can anybody point me to the trick how glm is computing the dispersion parameter in quasi-poisson regression, eg. glm(...,family="quasipoisson")? Thanks &regards, Sven

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 R-helpers, this mail is intended to mention a rather trange result and generate potential useful comments on it. I am not aware of another posts on this issue ( RSiteSearch("quasipoisson lmer version dispersion")). MUsing the exemple in the reference of the lmer function (in lme4 library) and turning it into a quasi-poisson or quasi-binomial analysis, we get different results,

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

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

Hello R-helpers, I would like to fit a non-linear function to data (Discrete X axis, over-dispersed Poisson values on the Y axis). I found the functions gnlr in the gnlm package from Jim Lindsey: this can handle nonlinear regression equations for the parameters of Poisson and negative binomial distributions, among others. I also found the function nls2 in the software package

Dear all, Using the quasibinomial and quasipoisson families results in no AIC being calculated. However, a quasi AIC has actually been defined by Lebreton et al (1992). In the (in my opinon, at least) very interesting book by Burnham and Anderson (1998,2002) this QAIC (and also QAICc) is covered. Maybe this is something that could be implemented in R. Take a look at page 23 in this pdf:

Hi everyone, I'm trying to perform the following regressions in order to compare linear vs. sigmoidal fit of the relationship between my dependent variable (y) and one explaining parameter (x2), both including the confounding effects of a third variable (x1): quasi-pois-lin <- glm(y ~ x1 + x2, family = quasipoisson(link="identity"), data=fit) quasi-pois-sig <- gnm(y ~ x1 +

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 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

Hi all, Cameron and Trivedi in their 1998 Regression Analysis of Count Data refer to NB1 and NB2 NB1 is the negative binomial model with variance = mu + (alpha * mu^1) yielding (1+alpha)*mu NB2 sets the power to 2; hence, variance = mu + (alpha*mu^2) I think that NB2 can be requested via negbin2<-glm(hhm~sex+age,family=quasi(var="mu^2",link="log")) Is

Hello all: I frequently have glm models in which the residual variance is much lower than the residual degrees of freedom (e.g. Res.Dev=30.5, Res.DF = 82). Is it appropriate for me to use a quasipoisson error distribution and test it with an F distribution? It seems to me that I could stand to gain a much-reduced standard error if I let the procedure estimate my dispersion factor (which

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 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'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)

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.

Dear R users, I have now tried out several options of obtaining p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and single-term deletions with subsequent chi-square tests (although I am aware that the latter may be problematic). However, I encountered several problems that can be classified as (1) the quasipoisson lmer model does not give p-values when

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

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

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