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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Validity of AIC When Comparing Models with Varying Dispersion Parameters
I'm currently making a binomial model with a logit link, which is parameterised as a quasibinomial since I'm allowing it to calculate the dispersion parameter. I was wondering, since changes to the ...
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How much dispersion is too much for quasipoisson regression?
Quasipoisson regression goes beyond standard poisson regression in taking into account overdispersion (whereby the dependent variable's variance is much greater than its mean). This is explained at ...
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dispersion of a negative binomial model
In R's, glm.nb summary, it says dispersion parameter $\phi$ is set to 1. When the model is
$Y \sim \text{Negbin}(\mu,\theta)$
where $E(Y)=\mu$ and $V(Y)=\mu+\mu^2/\...
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GLM dispersion based on Pearson residuals
In R's glm, the dispersion parameter can be automatically derived using a quasilikelihood model. When I manually compute it, the values are slightly different. The difference is very small and will ...
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How to model heterogenenous count data?
I have been trying different models to model highly variable count data (mean 8.5, variance 144.3), that is grouped by participant (each participant took between 4 and 16 tests). Within each ...
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What are the assumptions of beta-binomial models, and how do I test for them in r?
I want to model the effects of dispersal distance (disp) and reproductive rate (rep) on colonization rate, quantified as the ...
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Is this suitable for Negative Binomial Regression?
I am using SPSS to run a negative binomial regression for my research project. My dependent variable is 'Number of Days Went to the Gym' in a week, and my independent variable is income (along with a ...
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Running a Negative Binomial Regression with Overdispersion
I am not an expert on SPSS but have been developing my skills and believe I should run a negative binomial regression on my data, though I am not certain.
I am testing the number of days people go ...
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Is it possible to run a zero-inflated negative binomial model with complete separation? [duplicate]
I am trying to analyze how the number of events Y is influenced by three factors A (4 levels), B (2 levels) and C (2 levels) and the interactions between the three.
Initially trying a poisson ...
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From overdispersion to underdispersion: comparing linear regression models with DHARMa
I have been investigating the relationship between the occurence of certain weather phenomena and time. To aid me in evaluating the fit of my (simple linear-regression) models, I have been using the ...
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Difference between heteroskedasticity and overdispersion
Are both terms equivalent? They seem very similar to me. Or does one imply the other?
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Solution for Overdispersion in Poisson Regression
I have run poisson regression in SPSS (Generalized Linear Model), where BMI is my IV and length of stay (LOS) in hospital for certain disease is my DV (and it's a count variable).
When I run poisson, ...
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Why would a model indicate overdispersion without random effects but underdispersion with random effects? (and how to handle)
Overview: In my model building process, I fit both GLMs and GLMMs. I noticed that the GLMs suggested overdispersion in the data, while the GLMMs suggested underdispersion. How can I make sense of this,...
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How to account for overdispersion for GLMM with binomial distribution in R?
I am pretty new to R and am having some trouble finding a straightforward solution to overdispersion in a GLMM with binomial distribution. I have a few different questions listed here. I am mostly ...
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Generalized linear (mixed) model, binomial - help!
I work in biology and I´ve done an experiment exposing an invertebrate to a pesticide at different temperatures. One of my endpoints is hatching success of their eggs. The animals lay clutches of eggs,...
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dispersion parameter in Poisson models
When using a Poisson GLM, its dispersion parameter can be estimated as "residual deviance/degrees of freedom".
But when analyzing Poisson data with a Poisson model (i.e., not overdispersed), ...
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Overdispersion in a beta regression? (DHARMa package)
I'm trying to run a beta regression to predict my dependent variable Consistency, which has values between 0 and 1.
Here is the distribution of Consistency values in my dataset:
I originally tried ...
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Overdispersion tests for weighted binomial GLM(M)s
I'm running GLM(M)s on proportional data ([0,...,1] ) using a binomial family and weighted to number of trials.
ProportionFlowertoPod_Site.b = glmmTMB(PropFlowtoPod ~ Site_ID,
family = binomial,
...
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Count process with standard deviation proportional to its mean
What is (is there) the count process, which has its standard deviation proportional to its mean?
Note that I am not talking here about Poisson process, which has its variance proportional to mean. ...
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Zero inflated dependent variable and tree ml regression models
I wonder, if tree based ml models (e.g. xgb or random forests) are actually susceptible to zero inflated dependent variables (DVs) in the case of regression (in a sense the DV is at least bi-modal)? ...
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Why are the deviance residuals in my binomial GLM all zeroes?
I am currently trying to run a binomial GLM to investigate the influence of temperature (factor: 5 levels 20, 23, 26, 29, 32 degrees Celsius) and species (factor: 2 levels HA and AP) on the likelihood ...
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Minor over-dispersion: reasonable to proceed with standard poisson GLMM?
I am using a Poisson GLMM with glmer() from lme4 package in R. My data is ecological count data, and the model has one random ...
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Overdispersion in logistic regression --- Use beta-binomial?
I have some cell counts obtained via flow cytometry - simply put, I have the amount of positive cells (Successes) from the overall number of cells (Successes + Failures). Based on the data structure, ...
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calculating 'expected' rainfall for a period (e.g., month) when there is zero-inflation and over dispersion
I want to use 20 years of estimated precipitation data (maybe from CHELSA or CHIRPS datasets) to look at what the expected amount of precipitation is for different 30-day periods. The main purpose of ...
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Is the OLRE term meaningful in the negative binomial model? + Is overdispersion in the NB model an issue?
I'd like to ask three questions regarding the negative binomial (NB) regression / distribution.
The NB model with NB2 parameterization ($var(Y_{NB2}) = \mu + \frac{\mu^2}{\theta}$) is sometimes ...
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Outliers and possible dispersion in neg. binomial glmm residuals (DHARMa package)
I need help fixing the model I landed on through backwards step-wise elimination. I chose a negative binomial model because my variance seems much larger than the mean, with random intercepts from the ...
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WHy is the over dispersion in this poisson and quasi-poisson the same?
I have a zero inflated count data, on which I have run a poisson and quasi poisson reg using glm().
The output from a poisson model is as follows:
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How does one deal with linear regression with heteroscedasticity?
Suppose I have a dataset with outcome continuous. I applied various transformations on either covariate, outcome or both. I have also tried polynomial terms. I always get over heteroscedasticity when ...
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Biased Parameter Estimation in Poisson Regression
I read over here (https://aip.scitation.org/doi/pdf/10.1063/5.0040330) that "If the equi-dispersion is not met, the Poisson Regression is no longer appropriate to model the data. Moreover, the ...
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Interpretation of Dispersion Parameter and Intercept in ZINB Model
I am doing my first own data analysis and I'm new to more complicated mixed models. I fitted a ZINB model with the glmmTMB package with the following code:
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Interpreting Over-dispersion test for Poisson regression
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 ...
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Dispersion parameter in negative binomial
I simulate data from a Gamma-Poisson model in R as follows. The mean and variance of the negative binomial distributed counts are $a b=10$ and $a b (1+b)=60$.
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Does changing model due to overdispersion/underdispersion results in forking?
This is related to the post How much do we know about p-hacking "in the wild"?. The post does not clearly delineate the boundary between forking or not forking to me.
Suppose I have a count ...
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What is an alternative to Chi-Square for observed vs. expected variance suitable for repeated measures?
I want to test for individual-level side preference of a behaviour during an experiment, and found a way to do this using a chi-square test that uses the number of right (or left) turns out of the ten ...
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How do you test if the average of a population is the same as the variance of the same population?
What can be a statistical test to find out if a population has the mean equal to its own variance? I.e. Mean(X)=Var(X)?
I am interested in it because Poisson regression makes the assumption that the ...
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What better I use for Negative Binomial Regression with library(MASS) glm(family=negative.binomial) or glm.nb?
Hay, im a newbie and still need more learn. I have several question, I'm trying to create a negative binomial regression model using the R and library(MASS). But i'm still confusing what sould I use ...
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How to develop a negative binomial model where the overdispersion parameter varies as a function of one of the independent variables/covariates?
I am trying to develop a negative binomial model where the dependent variable is crash count, and the independent variables are traffic count and roadway length. Currently, with the below code, I get ...
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Are overdispersion and underdispersion in a binomial logistic regression model an issue if the model is not being used to make predictions?
If a binomial logistic regression model is being used strictly to identify variables that have an impact on the dependent variable but is not being used to make predictions, are underdispersion and ...
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When does a group specific dispersion parameter for the negative binomial distribution make sense?
If you have overdispersed observed abundance of multiple species including zero inflation the negative binomial distribution seems to be a reasonable choice. But if some species occur much more ...
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Residuals of GAM models not improving with poisson or ziplss, but better with negative binomial (help with high values)
I am running GAM models on species counts with lots of zeros and high values or high counts. Residuals under poisson family have a s-like curve on qq line with models not predicting lower and higher ...
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Meaning of "Overdispersion" in Statistics
I am trying to understand what "overdispersion" means in statistics.
Based on the Wikipedia page, "overdispersion" is defined as follows : "In statistics, overdispersion is ...
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Is there any R packages allow direct MLE estimation of dispersion in negative binomial distribution?
Using the built-in function, I can get
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What does the dispersion parameter means in negative binomial regression?
I am completely new to the topic of negative binomial regression and am unsure about what the output of my regression exactly means. Before I decided to use the negative binomial regression, i did ...
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Why fitting a Poisson GLM in an over dispersed dataset underestimate the standard error of the regression parameter?
It is claimed by many authors that if we fit the GLM Poisson model to an over dispersed dataset of count data, the standard error of the estimated coefficients will be under-estimated.
Could you ...
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Dealing with singularity and overdispersion in GLMM?
I'm running a GLMM through the lme4 package in R to detect differences in time spent feeding (response) before and after birth (my 2 categories in the variable inf_cat).
I started with a Poisson GLMM, ...
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How do I deal with ties when using rank-based normalizing transforms, e.g. Blom?
I would like to transform heavily skewed data with range (-Inf, Inf) and heavily zero-inflated into a form suitable for using GLMs for significance testing. Zero-inflation precludes the effective use ...
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For overdispered data, should the correlation matrix exclude zero?
I have 4 species and their distributions are overdispersed in space (i.e. lots of zeros). I calculated a Pearson correlation matrix and there is a lot of cluster around the 0s and 1s. Should I ...
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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 ...
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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) :
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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 ...
