similar to: Using quasibinomial family in lmer

After looking at 48 glm binomial models I decided to try the quasibinomial with the top model 25 (lowest AIC). To try to account for overdispersion (residual deviance 2679.7/68 d.f.) After doing so the dispersion factor is the same for the quasibinomial and less sectors of the beach were significant by p-value. While the p-values in the binomial were more significant for each section of the

Full_Name: Henric Nilsson Version: 2.3.0 Patched (2006-05-09 r38014) OS: Windows 2000 SP4 Submission from: (NULL) (83.253.9.137) When supplying an unavailable link to `quasibinomial', the error message looks strange. E.g. > quasibinomial("x") Error in quasibinomial("x") : 'x' link not available for quasibinomial family, available links are "logit",

Dear all, I've got some questions, probably due to misunderstandings on my behalf, related to fitting overdispersed binomial data using glm(). 1. I can't seem to get the correct p-values from anova.glm() for the F-tests when supplying the dispersion argument and having fitted the model using family=quasibinomial. Actually the p-values for the F-tests seems identical to the p-values 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

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

I am unable to calculate confidence intervals for the slope estimate in a quasibinomial glm using confint(). Below is the output and the package info for MASS. Thanks in advance! R 2.9.2 MASS 7.2-48 > confint(glm.palive.0.str) Waiting for profiling to be done... Error: dims [product 37] do not match the length of object [74] > glm.palive.0.str Call: glm(formula = cbind(alive, red) ~ str,

Hola a tod en s, mi compañero y yo intentamos ver la correlación de nuestros datos mediante regresiones logísticas. Trabajamos con proporciones (1 variable dependiente y 1 independiente) mediante modelos mixtos (los datos están agrupados porque hay pseudoreplicación). Hemos usado el paquete "lme4" y la función "lmer". Encontramos "overdispersion" en el resultado

Dear all, I try to consider overdispersion in a lmer model. But using family=quasibinomial rather than family=binomial seems to change the fit but not the result of an anova test. In addition if we specify test="F" as it is recomanded for glm using quasibinomial, the test remains a Chisq test. Are all tests scaled for dispersion, or none? Why is there a difference between glm and lmer

I've originally made 48 GLM binomial models and compare the AIC values. But dispersion was very large: Example: Residual deviance: 8811.6 on 118 degrees of freedom I was suggested to do a quasibinomial afterwards but found that it did not help the dispersion factor of models and received a warning: Residual deviance: 3005.7 on 67 degrees of freedom AIC: NA Number of Fisher Scoring

Hello list, i have a glm with family=binomial, link=logit but there is over-dispersion. So, in order to take into account for this problem i choose to do a glm with family=quasibinomial(). I'm not an expert on this subject and i ask if someone could validate my approach (i'm not sure for the tests) : quasi_glm(myformula,quasibinomial(),start=mystart) summary(quasi) # test t for

Hi, a while ago I posted a question regarding the use of alternative models, including a quasibinomial mixed-effects model (see Results 1). I rerun the exact same model yesterday using R 2.7.2 and lme4_0.999375-26 (see Results 2) and today using R 2.7.2 and lme4_0.999375-27 (see Results 3). While the coefficient estimates are basically the same in all three regressions, the estimated standard

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 am trying to automate the creation of tables for some simply analyses. There are lots and lots of tables, thus the creation of a user-defined function to make and output them to excel. My problem is that some of the analyses have convergence issues, which I want captured and included in the output so the folks looking at them know how to view those estimates. I am successfully able to do

Hi, I was wondering why anova() and drop1() give different tail probabilities for F tests. I guess overdispersion is calculated differently in the following example, but why? Thanks for any advice, Tom For example: > x<-c(2,3,4,5,6) > y<-c(0,1,0,0,1) > b1<-glm(y~x,binomial) > b2<-glm(y~1,binomial) > drop1(b1,test="F") Single term deletions Model: y ~

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

tryCatch() is good for catching errors but not so good for warnings, as it does not let you resume evaluating the expression that emitted the warning. withCallingHandlers(), with its companion invokeRestart(), lets you collect the warnings while letting the evaluation run to completion. Bill Dunlap TIBCO Software wdunlap tibco.com On Tue, Mar 6, 2018 at 2:45 PM, Bert Gunter <bgunter.4567 at

1. I did not attempt to sort through your voluminous code. But I suspect you are trying to reinvent wheels. 2. I don't understand this: "I've failed to find a solution after much searching of various R related forums." A web search on "error handling in R" **immediately** brought up ?tryCatch, which I think is what you want. If not, you should probably explain why it

In this real example (below), all four of the replicates in one treatment combination had zero failures, and this produced a very high standard error in the summary.lm. =20 Just adding one failure to one of the replicates produced a well-behaved standard error. =20 I don't know if this is a bug, but it is certainly hard for users to understand. =20 I would value your comments=20 =20 Thanks =20

You can capture warnings by using withCallingHandlers. Here is an example, its help file has more information. dataList <- list( A = data.frame(y=c(TRUE,TRUE,TRUE,FALSE,FALSE), x=1:5), B = data.frame(y=c(TRUE,TRUE,FALSE,TRUE,FALSE), x=1:5), C = data.frame(y=c(FALSE,FALSE,TRUE,TRUE,TRUE), x=1:5)) withWarnings <- function(expr) { .warnings <- NULL # warning handler will

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