Search Results

I did the over-dispersion test for my Poisson regression model in R, to check whether negative binominal is a better option. … I used stats package for conducting Poisson regression and AER package for testing overdispersion. dispersiontest(m2.int,trafo=1) Below is the output of the test Overdispersion test data: m1.int …

I use R for a poisson regression (GLM) to test for a difference in the number of years of schooling based on race (6 categories) and religion (3 categories), see structure of dataset and output below. … Hence, I conduct an ANOVA with an F-test for the model which then shows that the interaction term is not statistically significant. What is the correct way to interpret this result? …

How should I test for overdispersion in my situation? I tested overdispersion in a simple Poisson/negative binomial regression (without random effects) that I know how to fit. … The test suggests presence of overdispersion. However since these models do not take the clustering into account I suppose this test is incorrect. …

Now there are several methods of testing for overdispersion including Auxiliary regression (in R) Likelihood ratio test (in R) These involve testing for overdispersion in a fitted model. … My question now is, since I have around 100 possible covariates, do I first select covariates and then test for overdispersion or the other way around? …

block (Intercept) 0 0 Number of obs: 16, groups: block, 4 I have left out the fixed effect parameter tests and correlations for brevity. … . glm(rich ~ morph*caged, data=bexData, family=poisson) …

I am wondering if it is correct to use Likelihood ratio test, the same way like we do this with poisson and negbin models. Will critical value still have 1 df? (because of alpha as a difference). … And I would like to know - is it possible to compare ZIP and ZINB models with different parameters (not nested) with this test? …

The generalized poisson model saw an overdispersion of 290; while the negative binomial model saw a much lower overdispersion of 3.8. … The AIC of the generalized poisson is 2464 and that of the negative binomial is 2466. On running a likelihood ratio test, the genpois method is preferred. …

Is there a cutoff value or test for this ratio to be considered "significant?" … I found here this test for significance: 1-pchisq(residual deviance,df), but I've only seen that once, which makes me nervous. …

What I tried so far is to extract the predicted means and the shape parameter posterior distribution, compute the dispersion parameter, plot it, and test the probability that it is greater than 1: model0 … dispersion tests seem to simulate or compute dispersion parameters that can go below 1. …

However, even with the most abundant species, the model fails the goodness of fit test. … I also ran the analysis using an anova followed by a Tukey test from the raw data (as shown below), but then remembered that I would need to use a Poisson distribution for this data. …

My data shows a little bit of overdispersion (when fitted with quasi-poisson the overdispersion parameter is 5.01 and the overdispersion test in AER in R is significant). … So to account for overdispersion, I gathered that there are two options: 1) to use NB regressions and 2) to use cluster-robust SE when fitting the data with Poisson, such as the xtpoisson command in Stata …

first few rows of the data head(mydata) mydata$rank <- factor(mydata$rank) mylogit <- glm(admit ~ gre + gpa + rank, data = mydata, family = "binomial") summary(mylogit) I try to see if I have model overdispersion … But I not sure ... there is the package AER that makes the overdispersion test for Poisson and Binomial Negative and for Binomial there is any option? Thanks in advance! …

In modeling claim count data in an insurance environment, I began with Poisson but then noticed overdispersion. … I also examined NB, ZIP, ZINB, and Hurdle models, but still found the Quasi-Poisson provided the best fit. I tested for overdispersion via dispersiontest in the AER package. …

I wonder whether accounting for overdispersion in a GLM (Quasi - Poisson instead of Poisson family) has an effect on the model checking plots (plot of residuals against fitted values, a scale–location … I tested this and apart from the scale in the plots nothing changes. Is this hazard or is this always the case? …

Most of what I found was about the poisson or binomial distribution, and tests about significance e.g. … DHARMa::testOverdispersion, performance::check_overdispersion, and AER::dispersiontest only test for Poisson GLMs. My question is whether this overdispersion parameter value of 5.17 is too high? …