similar to: overdispersion

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 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

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 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 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

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

THANKS so very much for your help (previous and future!). I have a two follow-up questions. 1) You say that dispersion = 1 by definition ....dispersion changes from 1 to 13.5 when I go from binomial to quasibinomial....does this suggest that I should use the binomial? i.e., is the dispersion factor more important that the 2) Is there a cutoff for too much overdispersion - mine seems to be

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 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

Hi, I've noticed that if I disconnect or reconnect a phone from the net, Asterisk take long time to realize that (even more then 10 minutes). Is there a way to reduce this time, working on the configuration files? Thank you. silvia ------------------------------------------------------ Passa a Infostrada. ADSL e Telefono senza limiti e senza canone Telecom http://click.libero.it/infostrada

Hello, Given a vector I would like to rapidly identify duplicated non contiguous elements. Given for example c(1, 1, 2, 3, 2, 4, 5, 6, 4) I would like to get: FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE In fact I need to check this on the columns of a matrix! I can do that of couse with loops but is there any function already available? Thanks

Hello everybody I'm very new at using R so probably this is a very stupid question. I have a matrix of "p" columns and I have to calculate for each of them the "two sample t-statistic" and p-value and to save the results into two different vectors. I have divided my matrix into two submatrices: submatrix A containing the first "n1" rows (p columns) and submatrix B

thank you to both Adaikalavan and Patrick. on the basis of Adaikalavan example this is an example that point out my problems. >log_raise=function(num, exp, base){return(log(num^exp,base))} I would like to have optional fields, so some settings to be default parameters; if I declare the function as above the operator must of course input all the variables. Finally I could fix as follow.

Dear Partecipants to the list, I am searching a R function, which can calculate the inverse of one real value function. Does it exists a R code in order to make it? Many thanks for any kind of help and for Your availability. Enrico Foscolo ------------------------------------------------------ Passa a Infostrada. ADSL e Telefono senza limiti e senza canone Telecom

Hi all, I have count data (number of flowering individuals plus total number of individuals) across 24 sites and 3 treatments (time since last burn). Following recommendations in the R Book, I used a glm with the model y~ burn, with y being two columns (flowering, not flowering) and burn the time (category) since burn. However, the residual deviance is roughly 10 times the number of degrees of

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

Hi. I'm still facing troubles on my way. This time concerning ssl encryption. only dovecot-auth starts... imap and pop3 go down with this log dovecot: Mar 23 18:34:19 Info: Dovecot v1.0.rc27 starting up dovecot: Mar 23 18:34:20 Error: Temporary failure in creating login processes, slowing down for now dovecot: Mar 23 18:34:20 Error: imap-login: Can't load certificate file

I have functions using get.hist.quote() from library tseries. It seems that something changed (yahoo) and function get broken. try with a simple get.hist.quote('IBM') and let me kow if for someone it is still working. I get this error: Error in if (!quiet && dat[n] != start) cat(format(dat[n], "time series starts %Y-%m-%d\n")) : missing value where

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

I am running a binomial glm with response variable the no of mites of two species y->cbind(mitea,miteb) against two continuous variables (temperature and predatory mites) - see below. My model shows overdispersion as the residual deviance is 48.81 on 5 degrees of freedom. If I use quasibinomial to account for overdispersion the dispersion parameter estimate is 2501139, which seems