similar to: Quasi AIC

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

Hello R-list. I am a "long time listener - first time caller" who has been using R in research and graduate teaching for over 5 years. I hope that my question is simple but not too foolish. I've looked through the FAQ and searched the R site mail list with some close hits but no direct answers, so... I would like to estimate QAIC (and QAICc) for a glm fit using the

Dear R users, I would like to fit a glm with Poisson distribution and log link with a known dispersion parameter. I do not want to estimate the dispersion parameter. I know what it is, so I simply want to fix it at a constant for this and other models to follow. My simple, no covariate model is: Tall.glm<-glm(Seedling~1, family=poisson, offset(log(area)), data=tallPSME.df) I want to

Quasi-Poisson regression - using parameter estimates for QAICc Hello, I am using lmer (package lme4), for a GLMM, where I am modeling overdispered data with 1 random effect and several fixed effects. I want to use QAICc for my model selection, however I have 2 concerns 1) I don't know how to properly estimate the overdispersion parameter (c_hat), which is needed to calculate QAICc. I

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 trying to use MuMIn to compare all possible mixed models using the dredge function on binomial data but I am getting an error message that I cannot decode. This error only occurs when I use glmer. When I use an lmer analysis on a different response variable every works great. Example using a simplified glmer model global model: mod<- glmer(cbind(st$X2.REP.LIVE,

Hi there, I'm working with clustered data sets and trying to calculate log-likelihood (and/or AIC, AICc) for my models. In using the gee and geese packages one gets Wald test output; but apparently there is no no applicable method for "logLik" (log-likelihood)calculation. Is anyone aware of a way to calculate log-likelihood for GEE models? Thanks for the help, Bruce

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

Hi, I'm new to R and GLMMs, and I've been unable to find the answers to my questions by trawling through the R help archives. I'm hoping someone here can help me. I'm running an analysis on Seedling survival (count data=Poisson distribution) on restoration sites, and my main interest is in determining whether the Nutrients (N) and water absorbing polymer Gel (G) additions to the

Hi all, I know this a general question, not specific for any R package, even so I hope someone may give me his/her opinion on this. I have a set of 20 candidate models in a binomial GLM. The global model has 52 estimable parameters and sample size is made of about 1500 observations. The global model seems not to have problems of parameters estimability nor get troubles with the convergence of

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Hi all, I'm having trouble with the model generating 'dredge' function in the MuMIn 'Multi-model Inference' package. Here's the script: globalmodel<- glm(TB~lat+protocol+tested+ streams+goats+hay+cattle+deer, family="binomial") chat<- deviance(globalmodel)/59 #There we 59 residual degrees of freedom in this global model. models<- dredge(globalmodel,

Hi all, My replies within the forum aren't getting approved, though my emails always go through, so here is my reply to a question I previously posted (all questions and answers shown). Thanks, Cat I'm having trouble with the model generating 'dredge' function in the MuMIn 'Multi-model Inference' package. Here's the script: globalmodel<-

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,

I have a class called Duration that represents a unit of time. It has lots of time-related methods and an attribute that represents the duration in seconds. I want to store it in my db. I had a column type of NUMERIC, so if i save it, rails trys to coerce it to a float automagically and fails. I need to display it and store it as a number but treat it in the middle layer as a Duration so I can

Hi, I'm using a GLM with a quasi binomial error distribution and I would like to do a model selection method similar to step(AIC) to carry out a restricted search for the "best" model. I would like to know which of my 5 predictor variables would be included in the "best" model if I start with a 'full' model (fullbinom in this case). However, AIC can't be

Hello, Has anyone written up code to estimate for example a simple logit using quasi-likelihood? I know that glm() already does this, but I'd like to do some tinkering with the variance function beyond what glm() allows. I've scanned online sources and everyone seems to use glm(). Will take a crack at it if necessary but have zero experience w/q-likelihood and not that much with

Hello, I'm looking for a dataset for Quasipoisson regression. The result must be significantly different from the classic poisson regression. You can help me? Please It is for my last university exam Thanks a lot -- View this message in context: http://r.789695.n4.nabble.com/Dataset-Quasi-Poisson-tp3533060p3533060.html Sent from the R help mailing list archive at Nabble.com.

Hello all. I'd like to report near success with two games I've tested today. I used the latest wine (20040408). 1?) Arcanum ----------- I installed with wine; when I was prompted for the 2nd CD, I couldn't umount the first; so I cancelled the installation (as far as I remember the files were not erased; if they were I must have copied them somewhere before); then I copied the files

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