similar to: qcc.overdispersion-test

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 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

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:

Hello, I'm a grad student in the Intelligent Transportation Systems lab at Portland State Univ. in Portland, OR, USA. I'm trying to learn the basics of R to run a negative binomial in the near future, and so I ran a test regression on roadway crash data obtained from "Statistical and Econometric Methods for Transportation Data Analysis" by Washington et al (p. 250). I ran the

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

Hi, I am performing glm with binomial family and my data show slight overdispersion (HF<1.5). Nevertheless, in order to take into account for this heterogeneity though weak, I use F-test rather than Chi-square (Krackow & Tkadlec, 2001). But surprisingly, outputs of this two tests are exactly similar. What is the reason and how can I scale the output by overdispersion ?? Thank you,

I am looking for the correct commands to do the following things: 1. I have a binomial logistic regression model and i want to test for overdispersion. 2. If I do indeed have overdispersion i need to then run a quasi-binomial model, but I'm not sure of the command. 3. I can get the residuals of the model, but i need to then apply a shapiro wilk test to test them. Does anyone know the command

How can I eliminate the overdispersion for binary data apart the use of the quasibinomial? help me Eva Iannario ------------------------------------------------------ Passa a Infostrada. ADSL e Telefono senza limiti e senza canone Telecom http://click.libero.it/infostrada11gen07

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

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.

Dear All, I am working with linear mixed-effects models using the lme4 package in R. I created a model using the lmer function including some main effects, a three-way interaction and a random effect. Because I work with a binomial and poisson distribution, I want to know whether there is overdispersion in my data or not. Does anybody know how I can retrieve this information from R? Thank you

Dear All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My

I've been poking around with GLMs (on which I am *not* an expert) on behalf of a student, particularly binomial (standard logit link) nested models with overdispersion. I have one possible bug to report (but I'm not confident enough to be *sure* it's a bug); one comment on the general inconsistency that seems to afflict the various functions for dealing with overdispersion in GLMs

Dear list, I am needing to extract the estimate of overdispersion (deviance / residual degrees of freedom or c-hat) from multiple model objects - so they can then be used to compare the extent of overdispersion among alternative models as well as calculate qausi-AIC values. I have been unable to do this, despite consulting a number of manuals and searching the R-help. I am imaging that in

Dear all, I am using function glmer from package lme4 to fit a generalized linear mixed effect model. My model is as follows: model1 <- glmer(fruitset ~ Dist*wire + (1|Site), data, binomial) summary(model1) Generalized linear mixed model fit by the Laplace approximation Formula: fruitset ~ Dist * wire + (1 | Site) Data: data AIC BIC logLik deviance 68.23 70.65 -29.11 58.23 Random

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

[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

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-help-list, I have a problem in which the explanatory variables are categorical, the response variable is a proportion, and experiment contains technical replicates (pseudoreplicates) as well as biological replicated. I am new to both generalized linear models and mixed- effects models and would greatly appreciate the advice of experienced analysts in this matter. I analyzed the

Dear all, I am new to R and my question may be trivial to you... I am doing a GLM with binomial errors to compare proportions of species in different categories of seed sizes (4 categories) between 2 sites. In the model summary the residual deviance is much higher than the degree of freedom (Residual deviance: 153.74 on 4 degrees of freedom) and even after correcting for overdispersion by