# Questions tagged [overdispersion]

Overdispersion is when there is greater variability than there 'ought' to be in the data. Eg, the variance of counts is often greater than the mean, whereas the variance of a Poisson should equal the mean.

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### Modelling overdispersed rate data using a negative-binomial distribution

A quick overview of the analysis I'm wanting to do: I am wanting to analyze the relationship between habitat factors and the capture of my research species over a network of traps, in order to be able ...
2answers
48 views

### How to correct pattern in residuals when using a negative binomial distribution? Is it due to overdispersion?

I have a large dataset with the count data and I computed the following full model (covariates are all scaled) : ...
1answer
24 views

### How to work with continuous overdispered data?

I have troubles with analysis of my data. I analyze cross-sectional data about users activities and spendings from mobile game . I have paying and non-paying users, I need to explain what independent ...
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49 views

### How to correct conditional Poisson standard errors for over-dispersion

I want to estimate a conditional negative binomial model, which an economist might call a negative binomial model with individual fixed effects. I use Python and statsmodels, which has a conditional ...
0answers
50 views

### Python statsmodels, handling over-dispersion for Poisson Regression

I have a Poisson model (displayed below), where my $\epsilon_e$ term is designed to handle over-dispersion. I was curious if statsmodels has an easy way of returning a coefficient $\epsilon$ that fits ...
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### Is a (beta)binomial model fitting for my response variable?

I am working on the evaluation of a speech recognition system we trained. The recognizer basically is given a query utterance containing a single word and should find images containing the appropriate ...
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### Does pairwise correlation and multicollinearity matter in dispersion and zeroinflation model of glmmTMB?

I'm using glmmTMB to calculate beta-binomial GLMMs with nested and crossed random intercepts. I have overdispersed, zero-inflated data (assessed with Dharma). I use continuous terms in the very ...
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7 views

### Checking for Overdispersion in survey weighted data

I'm working with NHANES survey data. I'm trying to perform a survey weighted zero inflated Poisson model in R using a package called svyglm.zip. I want to check for Overdispersion. What statistical ...
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### Conf intervals on the dispersion coefficient of quasibinomial GLM or binomial GLMM with obs-level random effect

In this post I see how they calculate the confidence interval on the theta parameter of a negative binomial GLM: Confidence Interval for the Dispersion parameter of negbin distribution. I typically ...
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### What is the difference between conditional and unconditional fixed effects?

What is the difference between unconditional and conditional (fixed effects negative binomial) regression models? A similar question was asked for quantile regression here: What is the difference ...
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### Analyze count data errors of commissions - is my model fine?

Is my model doing a good job here? I have errors of commission in a SART-task as a dependent variable and stress index and its interaction with a group (2 groups) as an independent variable. The ...
0answers
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### Interpret the DHARMA simulation for the negative binomial regression

Our response variable is highly skewed and there is evidence of overdispersion as well. We used $pseudo R-squared$ and simulation using the DHARMA package to assess the quality of the model fit. How ...
1answer
37 views

### How to statistically test if the dispersion parameter in a negative binomial distribution is different?

I have a negative binomial distribution for two species. I have been able to fit (dummy) data to estimate mean (mu) and dispersion parameter (size). However, I want to statistically test if the two ...
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### Test for equality of distributions for paired (overdispersed) count data

I have overdispersed count data with an excess of zeros. I split them into a training and a test set and tried to reconstruct its distribution through the training set. I then sampled from these ...
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### Is it better to overcome model singularity or overdispersion? … or to run a Bayesian glmer?

We are using a dataset extracted from a predation measurement experiment. My main question is: Do individuals catch more prey when they have tumors? We measure different control and tumoral ...
1answer
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### How can I resolve overdispersion in a negative binomial model?

I started what seemed like a straightforward analysis, but I've gotten stuck with overdispersion in my negative binomial model. I would like to know which sites are different from each other in terms ...
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24 views

### Joint estimation of negative binomial dispersion parameter when measurements are independent per marker

Description of problem & experimental setup: In our experimental setup we have unique plasmids of which we measure the activity using RNAseq to get counts (generally follow negative binomial dist) ...
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### Overdispersion in logistic model

I'm relatively a newbie in R, and I've been trying to make a silly example of logistic regression to predict, according to Age and Sex whether someone dies of corona or not. I'm from Colombia, so my ...
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32 views

### Interepreting zeroinfl and glm.nb from my dataset

I am testing 4 predictors on overdispered count data with many zeros. The problem I am having is whether I have set-up my model correctly. my current model of ...
1answer
57 views

### How different from one does a dispersion ratio have to be to be considered significant?

I am in the process of conducting zero-inflated generalised mixed effects models with Poisson distributions and have been using the testDispersion() function of the DHARMa package in R to determine if ...
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### Error in a zero-inflated negative binomial model?

Following DHARMa diagnostic tests revealing zero-inflation (ratioObsSim = 32.663, p < 2.2e-16) and over-dispersion ...
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### Will usual tests state that data is overdispersed if response mean in poisson regression can vary greatly?

Assume I have 1000 responses from some count data, where each response follows the Poisson distribution with a mean (and variance) falling somewhere in the large range of 1 - 100. There is one ...
1answer
110 views

### What is the best way to deal with over-dispersion in a poisson GLMM?

I am currently in the process of trying to complete a poisson GLMM analysis with two fixed (with an interaction) and two random effects using the glmer() function of the lme4 package. Using the ...
1answer
101 views

### Sum of non-identical Bernoulli is overdispersed or underdispersed Binomial?

Extra-binomial variation is defined in this Oxford Reference source: Greater variability in repeat estimates of a population proportion than would be expected if the population had a binomial ...
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51 views

### How come a Poisson GLMM predicts a higher overdispersion than in the observed data?

I am using package brms in R to fit a Bayesian generalized linear mixed model in which: the response variable is the count of a phenotypic structure (e.g., toes) ...
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### How to perform negative binomial regression on gut microbiome data with over dispersion?

Thank you guys so much in advance! I got a table, containing the information of gut microbial composition of different groups, that is, the count data of different bacteria in each sample of different ...
1answer
121 views

### Interpreting results from a Poisson and Quasipoisson model

I used Poisson regression model to model how count of user actions on a website (dependent variable) are explained by website content (independent variables). The dependent variable distribution is ...
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### Trade-off between explaining variance and correcting overdispersion

I am fitting linear model. I happen to be working in R, and the specific model I'm fitting is a generalized additive model using the package mgcv, but I think all that is incidental to my question. ...
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### how to evaluate results with glmmPQL

First of all im thankfull for your attention. I have to evaluate the effect of 3 fixed effects in the vegetal coberture and i must use glmmPQL because my data has lineality condition problems and ...
1answer
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### GLMER Overdispersion and Error messages

I have a data set which involves 30 binomial absence/presences totalled for a ratio out of 1, which is the total score of a test out of 30 marks. The data requires fitting one of my predictor ...
1answer
172 views

### Modelling count data with extreme underdispersion - what distribution?

Suppose we have some count data, and we want to use a model that allows for "overdispersion" or "underdispersion" in the data (i.e., higher or lower variance than the Poisson distribution). Let $X$ ...
1answer
85 views

### How to choose the appropiate beta regression model type and variables?

Recently, I got my hands on modelling proportions [0,1]. Due to data type many of my variables are 0 and 1 inflated. Some of them are delicately affected by the bound values and some are heavily. I ...
1answer
195 views

### Interpretation of zeroinfl vs. glm.nb results

I am trying to test effects of 3 predictors on overdispersed count data with many zeros, and a Vuong test suggested that a zero-inflated neg. binomial model would fit better than a negative binomial ...
0answers
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### Appropriate model for count data when response variable minimum value is far above zero

CASE 1: I am trying to model count data; the response variable, y=c(12, 15, 34, 13, 12, 33,....,45) while the explanatory variables are location (binary, rural/urban), marital status, education level, ...
2answers
501 views

### Overdispersion in fitted generalized linear model with insignificant regression coefficients

Overdispersion is the phenomenon of having data that is more variable than its model assumes. Overdispersion can occur when the model in question has inseparable mean and variance parameters. If I ...
1answer
430 views

### Confused about over dispersion for my beta distribution

I have percentage data so I am using a beta distribution and I want to do a mixed-effect model so I am still trying to decide between glmmTMD or the brm packages. I saw somewhere that some ...
0answers
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### How to analyze data for spatial aggregation and niche overlap with no specific GPS points over time?

I study soil insects, and sample monthly for insects. Each month, I sample at 8 different sites. Each site is divided roughly into 4 meter square quadrants (shown in figure). From each quadrant, I ...
1answer
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### Looking for a discrete distribution with a specific mean-variance relationship

Say we have some counts $Y$ for which the mean-variance relationship is $$Var[Y] = \alpha E[Y] + \beta E[Y]^2.$$ From this, we can say that: If $\alpha = 1$ and $\beta = 0$, then $Y$ can be ...
0answers
434 views

### How to use quasi-Poisson model after overdisperson with glmer(mydata,family = poisson(link = 'log'))?

I have to fit my data with Laplace glmm with random effect using poisson distribution error. ...
1answer
205 views

### beta binomial to reduce overdispersion for binomial data (zero inflation)

I know that a negative binomial model is often use to solve the problem of overdispersion in count data (poisson regression). Now, someone said that a beta binomial model can also be used to solve the ...
0answers
148 views

### What actually happens when we model a frequency instead of count (POISSON GLM)

First of all, I am using R. I know that we can model a frequency-response variable with a poisson regression, if we remember to weight it, so that the variance doesn't get affected by it. I am not ...
1answer
1k views

### Is an overdispersion parameter of 5.17 for GLMM with Beta family too high to yield reliable results?

I'm running a generalised linear mixed model with beta family on the effect of overhead cover (proportion ∈ (0,1)) on the proportion of birds scavenging from carrion left out in nature (proportion ∈ (...
1answer
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### How to prove overdispersion in Poisson regression?

For $Y_i$ independent $\sim \mathcal{Poisson}(λ_i)$, $i = 1, \dotsc,n$, I want to assume $λ_i$ has mean $λ$ and variance $σ^2$. With this I want to show the following, but I am not sure how to show ...
1answer
632 views

### Determining overdispersion of count variable in bayesian model (brms)

I am trying to determine whether my response count data are too overdispersed for a (brms) Bayesian poisson model. I constructed a poisson-generated response variable with low and high levels of noise/...
1answer
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### Does one need to run a poisson regression to estimate the scale parameter before using negative binomial regression?

The negative binomial has two parameter in its distribution. Neg bin has a scale and a probability parameter. I’d imagine the scale parameter estimated in poisson regression is only one of them. Does ...
1answer
119 views

### Why ever use a quasipoisson model instead of bootstrapped poisson GLM?

A poisson GLM and a quasipoisson regression model will given identical point estimates for the beta parameter of the linear predictor. The quasipoisson model is typically used when there is ...
1answer
51 views

### Is constructing a simulated model for a fixed SNR possible in binary regression?

When studying variable selection techniques like step-wise regression and the LASSO, a few studies employ a signal to ratio measure in order to control the amount of variability in a simulation. ...
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### Hypothesis Testing with Chi-Squared: Is Overdispersion a concern?

Assume an AB test design, with one experimental group, one control group, and an anticipated effect on a conversion rate. The chi-squared test only takes as input successes/trials for each group. ...
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53 views

### Fish counts and Poisson [closed]

I am having troubles with fitting a Poisson distribution to my data, let me explain: I have fish counts of different species from a closed list, at 5 sites, 2 different depths, and across 10 years. ...
1answer
310 views

### Overdispersion vs Tweedie

I am dealing with data that could possibly be overdispersed and I am looking at fitting a GLM with a quasi distribution. As far as I understand, when we fit a glm ...