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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Very different scale parameter estimates in Poisson regression
The background: I'm analysing survival data using a Poisson model. I've splitted the data on 2 time-scales (attained age and calendar year). Attained age is modelled using flexible parametric ...
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Overdispersion in a binomial GLMER model
I'm having trouble accounting for overdispersion in a binomial GLMER (lme4 package) - I'd read through other posts on the topic but haven't found anything that solves my problem. I tried adding an ...
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Dispersion parameters in GLM
I'm trying to find the motivation behind the extended form of the exponential family of distributions in the fundamental paper on GLM by Nelder and Wedderburn (Generalized Linear Models, J. R. Statist....
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Quasipoisson or negative binomial glmm with differing dispersion by group
I have a set of count data, which look something like this:
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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 ...
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Overdispersion problem in a quasi-binomial GLM (for proportional data)
Below is the summary of a GLM I built for a response variable which is proportional (derived from count data). My only predictor is a continuous one (environmental measurement). And my sample size is ...
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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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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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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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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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Can we mix conclusions from Poisson and Quasi-Poisson?
Currently I'm working with ecological studies, where my response is a count variable. I need to estimate several models, each one represents a city. Afterwards I aggregate them to obtain meta-analysis ...
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How to recalculate variance/covariance matrix adding a overdispersion term in glmm?
When models suffer from overdispersion, a solution is to calculate the dispersion parameter (using c_hat or dispersion_glmer in R for example). Then multiply the variance/covarianze matrix of ...
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Quasi Poisson model quasi-likelihood
Wikipedia (https://en.wikipedia.org/wiki/Variance_function) gives this as the general definition of quasi-(log)-likelihood:
$$ Q(\mu,y)= \int _{y}^{\mu} \frac{y-t}{\sigma^2 V(t)} dt$$
In the case of ...
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Non-constant overdispersion (overdispersion parameter depending on covariate) in negative binomial regression
In a negative binomial regression (daily number of cases of a disease over time), after controlling for several factors and experimenting with several functional forms, my results still don't look ...
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Validation of poisson GLMM corrected for overdispersion with OLRE
I am fitting a random effects model to some ecological count data (abundance). The aim is to analyse the effect of influence of chemical pest control on insect population. I run the analysis on ...
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Can including an observation level random effect (OLRE) create nested random factors in a GLMM?
I have data from an observational study on insect parasitism rates from 42 sites. Some sites were sampled only once and others were sampled multiple times across different years. For each sampling ...
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Pseudoreplication in a split-split plot design on count data in R
Agricultural Experimental design: a split-split plot in which a field is divided into 3 replications; each replication is divided into 2 to apply different pesticide spraying programs and each spray-...
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Modelling overdispersed counts - past negative binomial
I'm modelling overdispersed counts. I began using a GLM with Poisson error structure, then moved to quasi-Poisson, and then finally negative binomial. The residuals versus fitted values plot is still ...
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binomial confidence including run to run variation for overdispersion
I'm trying to determine the model to correct a confidence interval (binomial proportion for example) but to also include overdispersion effects that arise from a Run-to-Run variation.
Example,
case ...
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Should I use a beta-binomial or binomial glmm?
I have several data sets on wildlife disease incidence. One of the issues with my dependent variable is that it represents only current infection status, therefore 0 (no disease) can represent either ...
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Multilevel model specification for count data (with overdispersion) in R
I would like to specify a multilevel model including the following variables:
DV: count data, i.e. a score value between 0 and 13 (resulting from an additive index) for each individual
Individual ...
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Comparing overdispersion distributions
I am dealing with handling overdispersed count data (Poisson distributions fails to fit).
I need to compare three different mixtures (Gamma, Log-Normal and Inverse Gaussian) of Poisson rate parameter ...
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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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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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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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Adjusting for clustering and overdispersion in count models
My question is specific to the estimation of glm's and correcting for 'clustering' in a quasi-experiment (difference-in-differences). My outcome is counts of crimes....
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glmm with proportion data and high overdispersion
I have data of time budget behaviors in form of relative frequencies (Nr specific behavior/nr of total behavior observed). The best would be to perform a multinomial analysis, but it seems too hard to ...
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How is the F-test superior to the $\chi^2$-test for handling overdispersion?
I am interested in analyzing data from group randomized trials: a group randomized trial involves recruiting groups, like children within a school, people within a community dwelling, bats in a cave, ...
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Using GLMs with gamma distribution and negative predictors
I'm currently trying to investigate relationships between habitat characteristics and animal abundances using GLMs. I've gone through the process of whittling down possible predictors and have a final ...
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underdispersion in a binomial GLMM
I am trying to analyze data from an experiment in which I measured the learning of a colour preference in birds under two treatments. 40 Individuals were organized into 8 groups, and 4 groups were ...
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How can I deal with overdispersed count data if I have a nested design?
I am trying determine whether pollen tube counts differ between nectar-robbed and un-robbed flowers. Pollen tube counts are nested within plant (multiple flowers of each type sampled from each plant) ...
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Fitting a complex model of variance-vs-mean for quasi likelihood models? (in R)
I wish to deal with over dispersion of a Poisson model. Negative binomial (glm.nb), and quasi likelihood models (family=quasi in glm) do not offer a flexible enough structure of the variance-vs-mean ...
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Overdispersed Poisson fit data
I'm dealing with traffic volume count data. The Poisson is a good fit for the data when I determined using chi-square test but the dispersion index is slightly overdispersed.
How to deal with this?
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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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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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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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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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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 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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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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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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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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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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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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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, ...
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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 ...
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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.
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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 ...
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Overcoming model singularity in overdispersed data set
I am analysing a data set that is created from walking transects and recording counts for each group size of animals observed. Each transect has 41 repeats, which was approximately 80% zeros. However, ...
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