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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Accounting for overdispersion in binomial glm using proportions, without quasibinomial
I am doing binomial GLM using relative abundance, for example:
model<-glm(cbind(number_pres,number_abs)~Var1+Var2+Var3+Var4..., family=binomial, data=Data). My sample size is about 700, and I have ...
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How to improve the fit of a zero-inflated, negative binomial glmmADMB model
I have been trying to fit count data that is zero-inflated and overdispersed using generalized linear mixed models. My research led me to the glmmadmb function in ...
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How would you go about modelling Gamma GLM with overdispersion?
In R, you can use quasipoisson or quasibinomial for overdispersed Poisson or Binomial GLMs. But what if you have overdispersed Gamma GLM or other continuous variables?
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Confidence Interval for the Dispersion parameter of negbin distribution
Can anybody show me, how to find a CI to Dispersion parameter of negbin distribution in R?
Iknow how to calculate the point estimate but I do not know how to find the confidence interval
so, what is ...
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How to model zero inflated, over dispersed poisson time series?
I am trying to model weekly disease counts in 25 different regions within 1 country over a ten year period as influenced by temperature. The data is zero inflated and over dispersed.
I am most ...
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Is it appropriate to account for overdispersion in a glm by using a quasi-binomial distribution?
I have several sets of count data (as below) that are overdispersed. The overdispersion likely comes as a result of the number of zeros in the data which I understand means the paramater estimates in ...
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Testing the assumptions of null models when using likelihood ratio tests to compare mixed-effect models
I have ran a Generalized mixed-effect model using the glmer function in the lme4 package. To appraise the significance of the model, I am using a likelihood ratio test. For example, if testing the '...
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Overdispersion tests dependence on used covariates in Poisson model
One of the shortcomings of the Poisson regression model is that the mean, conditional on the independent variables should equal the conditional variance. If the observed variance is larger (smaller) ...
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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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Variance of multinomial and Dirichlet-multinomial distributions
I have an application where I would like to sample from a multinomial distribution, but I am concerned that the variance will be too low. As an alternative, I am considering the Dirichlet-multinomial ...
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How to test over-dispersion for a compositional data in R?
I would do an over-dispersion test for a compositional data set (I don't have the original count values) for choosing later an appropriate regression model. Here is an example of my data set:
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How to deal with unobserved heterogeneity in binary logistic regression ? (using SPSS)
I have performed a binary logistic regression with SPSS.
I am trying to deal with the phenomenon of unobserved heterogeneity but actually I have not fully understood how to test it and how to deal ...
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Machine Learning methods / Regression Trees for Longitudinal/Panel Count Data
I have a data set where the dependent or the response variable is a non-negative and integer variable and exhibits over-dispersion where the variance is greater than the mean. Below is the figure
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Overdispersed Poisson regression
In Gelman and Hill 2007 (http://www.stat.columbia.edu/~gelman/arm/), they mention that adding an $\epsilon \sim N(0,\sigma^2)$ term to a Poisson regression can be used to account for overdispersion.
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Existence of conditional conjugate prior for the dispersion parameter of a negative binomial model
Is there a conditional conjugate prior distribution for the dispersion parameter of the negative binomial distribution given the mean rate parameter, if I parameterize it in this manner
$$f(y_i; \mu, \...
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overdispersion test for poisson distribution in generalized linear model [duplicate]
I run the generalized linear model on Poisson distribution with offset term to account for the differences in sampled area. The response variable is the count of aquatic insect larva and the ...
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GLM: Grouping/categorizing when checking for over dispersion [closed]
I am having a lot of trouble understanding how best to check for over dispersion in a model with many possible explanatory variables.
I have a data set consisting of one response variable and 4 ...
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Should one use the same overdispersion parameter when comparing Binomial models?
McCullagh & Nelder, 2nd edition, p 91 claim that to make comparisons "fair", it's best to use a single estimate of overdispersion parameter, usually derived from the most complex model. I noticed ...
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Calculating QAICc and Averaging GLMM models with various overdispersion
I am having difficulty figuring out how to calculate a dispersion parameter to calculate QAICc for a GLMM with a binomial fit.
I have tested for overdispersion using this code:
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Simulating an overdispersed negative binomial distribution
I have RNAseq data in the form of normalized counts. The counts themselves follow an over-dispersed negative binomial, and I would like to generate random data which replicate these distributions. For ...
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Interpreting dispersion parameters of poisson GLMM with count data
I am working with count data and trying to understand if my model fit is acceptable for this poisson Generalized Linear Mixed Model:
Richness.glmer<-glmer(Richness ~ Unit.type + plot.type + (1|NFI....
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Overdispersion in GLM with Gaussian distribution
To check for overdispersion in GLM with a Poisson distribution one can compare the residual deviance with the residual degrees of freedom. If they are equal the Poisson error assumption is appropriate ...
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Can there be overdispersion in a logistic regression model where each observation represents a single Bernoulli trial?
A friend and I are having a dis-agreement about over-dispersion in binomial/logistic regression glm modelling. We have structured our data so that each observation represents 1 Bernoulli trial (so the ...
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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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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 binomial regression model in R - correct formula, significance testing, and over-dispersion
I'm using generalized linear models to test for the effect of various predictors on some binomial data. My response is a binomial vector of successes and non-successes.
I want to test whether my ...
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Relationship between dispersion statistic and variance in count data models?
I am struggling to get my head around the concept of data dispersion, particularly relating to count models.
Take for example, the Poisson regression model, I will often read that if the variance of ...
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Individual level random effect for over dispersion
In glms we can use a quassipoison fudge factor to account for over dispersion in our poisson models.
In glmms we can add an individual level random effect (e.g. id)...
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Analyze count data that does not fit glm - Overdispersion
I am working with camera-trap data on mammals. My data looks like this:
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c-bind on proportion data, quasi-binomial GLM: how to remove effect of sample size
I'm studying a colonial organism, and my hope is to compare differences in percent survival between three treatment groups. The results are clear, there is a 55% difference in survival between ...
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Adding a square root link function to an overdispersed negative binomial GLM
I'm analyzing nematode count data (80 data points) from a randomized block design in which I have two factors with both four levels (Plant and Inoc). The data show heavy overdispersion when analyzed ...
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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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Beta-binomial logistic regression model for binomial data with small samples
I have fitted a nonlinear beta-binomial logistic regression model on data y_i:
y_i ~ beta-binom(n_i,mu_i,\Phi)
where mu_i = exp(\eta_i)/(1+exp(\eta_i)) , and \eta_i=\beta_0+\beta_1/(1+exp(\beta_2x_i ...
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Overdispersion in poisson glm
When calculating the dispersion deviance/degrees freedom I get the value 1.8. Is it absolutly necessary to carry out the glm using quasipoisson? What is deemed 'significantly overdispersed' ?
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Variance of the influence function of the parameters of an over-dispersed poisson model
Assume an over-dispersed Poisson model:
\begin{align}
E[C_ij] &= m_ij \quad\text{ and } \\
{\rm Var}[C_ij] &= ∅E[C_ij]= ∅m_ij \\
\log(m_{ij}) &= n_ij \\
n_{ij} &= c+ α_i + β_j \quad\...
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using a dummy to indicate zero values of a overdispersed continuous predictor variable [duplicate]
I have a predictor variable that has many zeros. The predictor variable is simply a count of the occurrences of some behavior. The zeros are qualitatively meaningful. I'd like to use a log ...
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Use law of total variance to find unconditional variance of overdispersed Poisson?
First, I need to prove that the distribution of a RV X, where X|lambda ~ Pois(lambda), and lambda ~ gamma(a, B), is a negative binomial. I know that it is, but why negative binomial instead of another ...
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How does glmnet handle overdispersion?
I have a question about how to model text over count data, in particular how could I use the lasso technique to reduce features.
Say I have N online articles and ...
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How to model overdispersed percentage data?
this is my first post so let me know if you need more information. This is a pretty general question for now, but I am not sure how to approach this.
The data I have is from an ecological study. In ...
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Analysing overdispersed data with generalised linear models
Let's say I have an explanatory variable and a response variable that represents counts. I want to see if the explanatory variable can predicts counts. I'm aware the response variable is overdispersed....
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How to know dispersion if $\mu$ is close to or below 0 (chance-corrected beta-binomial model)
Background
In sensory science, "replication" means having a panelist in a taste panel do multiple rounds of the same test. You cannot just count those additional rounds as additional panelists, ...
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What is the correct way to identify overdispersion?
I calculated the dispersion parameters for each of two blocks in a hierarchical logistic regression. I am not quite sure how to interpret the dispersion parameter.
First I calculated the parameters ...
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Enormous SEs in zero-inflated negative binomial regression
I have overdispersed count data where the outcome is events (occurrence of a rare disease) and the covariate of interest is season. The unit of analysis is the number of events occurring in a country-...
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Understanding over-dispersion as it relates to the Poisson and the Neg. Binomial
I am developing a Poisson-family glm model in R for a dataset that I have. This dataset has 650 entries with two measures of exposure. The model, though not that relevant to the question, is:
$$\ln(E(...
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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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Very large theta values using glm.nb in R - alternative approaches?
While analysing the effect of environmental data on the activity of an animal species (the latter given as count data) I am fitting negative binomial GLMs with one predictor using the MASS library in ...
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Definition of dispersion parameter for quasipoisson family
I try to model quasi-poisson family in bugs language, to handle overdispersion. According to Introduction to WinBUGS for ecologists, this is done by:
$log(\lambda_i) = f(x_i) + \epsilon_i$
$N_i \sim ...
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What is the global model?
I'm trying to calculate c-hat, the overdispersion parameter for a QAIC model set. According to Burnham and Anderson, you're supposed to calculate c-hat on the global model. Is the global model the ...
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Dispersion parameter of negbin distribution
Can anybody show me, why the dispersion parameter of the negative binomial distribution is taken to be one? In the Poisson case you can show that $E(y)/V(y)=\mu/\mu=1$ which is called equidispersion. ...
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Extra negative binomial distribution or extra binomial distribution
I doubt how to treat my outcome variable, and consequently, which regression analysis I should apply. I'm working with a count variable, namely the times a person said "I don't know" on a total of 45 ...
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