Search Results

I can't use Poisson because the total number of "trials" varies per sample point (relative abundance account for this), and I'd prefer not to simplify to presence/absence. … I did a few tests and spatial autocorrelation of residuals might be a minor issue (in car Durbinwatsontest showed reject null of no autocorrelation, but in gstat variogram the semivariance hovered around …

I used Poisson regression but since there is overdispersion, I moved to Negative Binomial Poisson regression. …

Using the testDispersion() function of the package DHARMa I found my data to be significantly over-dispersed (ratio = 1.877, p-value = <2.2e-16) so as a result attempted to use the glmer.nb() function … My problem is that the model using this function still produced a significant dispersion test (ratio = 0.8817, p-value = 0.024). …

I am confused as to which test works best and the R code for it. LLM, GLM. Should I use a random intercept? … I have tried these: #1 #visitIdx is a count variable so # Fit a GLMM with Poisson distribution library(lme4) model_glmm <- glmer(visitIdx ~ sex + (1 | id), family = poisson(), data = Gbirds_sex) summary …

I would like to plug in the positive tests for each county in California to get an outcome IRR. … Using STATA I was able to obtain this formula: 38.84652 + 1.000044*(positive tests) …

I want to test the number of varroa mites in individual apiaries is higher in the south of England vs the north. I'm working with count data (mites) and categorical data (location). … I used a binominal GLM, however the output produces a EXTREMELY high overdispersion (around 1600) is there any way to handle this? Or should I be using a different statistical test? …

Using testDispersion() in DHARMa to check the dispersion we get a very similar result: DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput dispersion … I am including some more tests from DHARMa from my neg-binom model in the case the answer somehow lies there: …

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. …

I am fitting a glmm model using the R library glmmTMB for predicting a count response variable with excess-zeros and overdispersion (nbinom2> Poisson). … Choosing between the two becomes a crucial analytical design conditioning the interpretation of the results, conditional to the study design and tested hypotheses. …

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? …

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. …

and negative binomial regression and then tested for over/under dispersion, which exists in both models. … I ran the likelihood of ratio test to compare the zero-inflated model with the normal model and it favors the zero-inflated. …

I have long-term collection data, and I'd like to test, whether the number of animals collected is influenced by weather effects. … ; although my data do show considerable overdispersion, this did not help as the residuals became even more ugly (see Fig. 2) I fitted models without random effects, with quasi-Poisson glm and glm.nb …

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 am currently working with pandas and scikit-learn for Poisson Regression (now turned negbinom to address Overdispersion) to model count data of y (ticket count) with each day of the week serving as a … For the model, I have used the first 41 days as the training set, with the next 38 days to serve as the test set. …