# Questions tagged [quasi-likelihood]

In GLMs, quasi-likelihood estimation is a way to allow over- or under-dispersion by choosing an appropriate variance function.

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### How to Calculate Sample Size Requirements for Quasi-Poisson / Negative Binomial AB Test in R

I'm doing an AB test of two conditions, where the success metric is looking for an increase in the number of users taking a particular action each day. The variance is hugely larger than the mean, ...
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### Does poisson penalized quasi likelihood regression use biased estimators?

A professor told me the Poisson glmmPQL (mixed-effects/hierarchical) regression gives biased estimates. The paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3886992/ from PLOS One says that ...
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### What is the distribution in Quasi-Poisson regression?

For Poisson regression, the assumption is that Y has a Poisson distribution. Is the same assumption true for Quasi-Poisson regression?
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### GLM model interpretation of quasi poisson incorporating dispersion in R

I am fairly new to GLMs, and am currently working with a quasipoisson distribution due to overdispersion. I have read through the relevant sections of Mixed Effects Models and Extensions in Ecology ...
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### What is the impact of excess zeros on poisson regression coefficient estimates?

The background I have a dataset with some zeros - based on how I segment my data, it is either 50% of the observations or 80% of the observations. The data is not actually count data, but from what i ...
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### How to fit a zero-inflated quasi-poisson distribution to a continuous variable using GAM [closed]

I am trying to fit a GAM to a continuous variable which is zero-inflated. However, since my variable is continuous, I am not able to use ziP() for a zero-inflated quasi-Poisson. Is there someway to ...
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### Quasi-Poisson - Goodness of fit

Can I use likelihood ratio chi-squared tests to test the goodness of fit for quasi-Poisson models?
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### Why the absence of probability distribution for using Quasi-likelihood?

We can compare $2\cdot(\text{difference in log-likelihood})$ to a chi-square distribution but why can't we find or invent a new distribution whose test statistics are some function or form involving ...
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### Interpretation of fractional regression (GLM quasibinomial with logit link) coefficients

I am writing a research paper commenting theresults of the following regression, which is a GLM quasibionomial regression with a logit link (the outcome variable capfactor ranges between 0 and 1). <...
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### Quasi/Complete separation due to huge and infinite values

(R statistics) My question is regarding this warning. My data contains patients and healthy subjects. Exponential decay is my outcome measure. I have a example dataset here I managed to run ...
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### Is my overdispersion too large in this quasibinomial model?

I have used a quasibinomial model on my data, but my overdispersion coefficient seems to be too large with a value of 40.78776. ...
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### How does one compare two nested quasibinomial GLMs?

Lets say I have two models: Model 1 and Model 2, both of which are used to fit a quasibinomial GLM on some 0/1 response data (that I believe has overdispersion, hence quasibinomial GLM instead of ...
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### Likelihood ratio tests for quasi- models

I have been playing around with over-dispersion in binomial data and looking into qausi-binomial models as a solution. When comparing binomial models through the change in deviance, I can reproduce ...
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### Trouble interpreting GLM Quasi-poisson

I'm doing a count model on the rate of exonerations over prison admissions before and after 1992. I understand that Year > 1992 is the change in intercept post-...
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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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### How to interpret glm output for quasi-binomial model

I am having difficulty interpreting the output for a quasibinomial model. My first issue is that I have used the function 'autoplot' to test assumptions, and the normal Q-Q plot is skewed: I am ...
211 views

### General linear mixed model in R which will fit quasi family [closed]

I am trying to run a GLMM with a quasibinomial family (my data is 0 inflated and I have a negative min x value), but am receiving this error message as quasi families cannot be used in glmer: ...
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### Which kind of diagnostic plots for count data? [duplicate]

I know that for an lm model is enough to run plot(model_lm) to get diagnostic plots. I am dealing with high-dimensional count ...
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### Interpretation of Conway Maxwell Poisson regression coefficients

For a simple Poisson regression, I would interpret the coefficient as (1-exp(coef))*100 for percentage change in y given a unit change in x So for a Conway-Maxwell Poisson regression, Sellers and ...
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### Are over-dispersion tests in GLMs actually *useful*?

The phenomenon of 'over-dispersion' in a GLM arises whenever we use a model that restricts the variance of the response variable, and the data exhibits greater variance than the model restriction ...
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### Model Quasipoisson interpretation and validation

I am currently doing my Master thesis with evaluating my results in R. I am stuck on my analysis of my glm with quasipoisson. I am analysing influencing variables on the dormouse abundance in 2 types ...
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### Compairing the fit of quasi-Poisson and negative binomial models

Is there any way to compare the fit of quasi-Poission and negative binomial models in R?
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### Compairing quasi-Poisson and negative binomial fits in R [duplicate]

Are there any R functions that allow you to easily compare quasi-Poisson and negative binomial models to determine which error distribution is more appropriate for your data? Or - does this require ...
353 views

### Why can't you fit a quasi-Poisson in lme4? [closed]

Is there a philosophical reason why lme4 does not allow you to fit a quasi-Poisson model while it will allow you to fit a negative binomial model? I do not see any reason why any glmm package should ...
140 views

### 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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### Comparing goodness of fit of quasi-Poisson regression model predictions vs. unknown forecast

I have built a quasi-Poisson regression to predict sales of different products based on a number of explanatory variables, with an offset term for the number of days each product was on sale. To ...
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### Calculating odds ratio for quasibinomial glm

I have a quasibinomial glm and I'd like to get odds ratios for each of the predictors. Is it acceptable to use the 'oddsratio' package in R in the same method that you would for a binomial model (e.g. ...
2k views

### Comparing quasibinomial glms in R

I have some data that are proportions that I am trying to fit a glm to. As the data are proportions someone suggested that the quasibinomial is the correct family of glm to go for. I am now trying to ...
142 views

### How to compare nested quasi poisson models?

I have the following nested quasi poisson models for overdispersed count data: ...
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### Overdispersion. Is it ever appropriate to have a non-linear variance correction from Poisson to a Quasi-Poisson?

This is my first Cross Validated post. Please let me know if this is too specific/innapropriate for the site. I am building a Poisson/quasiposson model with one predictor. The response variable ...
469 views

### Overdispersion in Count Data (Poisson model)

There is more than one solution for the problem of overdispersed count data. One is to use a quasipoisson model. One is to use a negative binomial model. One is to use a mixed-level model with subject-...
517 views

### How to interpret the coefficients in a fractional logit model?

I have some data that is bounded between 0 and 1. I have used the command myfrm <- frm(y, x, linkfrac = 'logit') to fit a regression model with the bounded ...
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### Quasi-likelihood/Quasi Poisson

I'm facing this new concept: the quasi likelihood. I'm looking for some clear explanation of what it is. I have a very basic knowledge about this, so I need to go step by step very slowly. I ...
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### What is variance argument in quasi-generalized linear models?

The glm function in R can estimate models from a number of families, including the quasi family. The ...
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### What happen when model selection ranks null model as the best one and there's another model that is competitive?

I'm analyzing the proportion of marked chicks vs. the number of chicks that were recaptured at one moth of age (not possible to use conventional capture-recapture analysis because we don't have a ...
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### Quasi-likelihood and complementary log-log link

I need help to understand how R computes the adjusted value when I use the quasi-likelihood distribution and complementary log-log link with variance function mu(1-mu). My data are rates from ...
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### purpose of using quasi likelihood [duplicate]

Based on MLE (Maximum Likelihood Estimator), we knew that the puprose is to maximize the likelihood function. Then how about quasi likelihood ? Is quasi likelihood just modelling between mean and ...
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### Residual deviance: Poisson versus Quasi Poisson

There are numerous posts that have explained residual deviance and parameter estimates for the quasi Poisson. But since there is no probability distribution pertaining to the quasi Poisson and hence ...
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### What exactly is a quasi-likelihood model? [duplicate]

I tried reading Wedderburn (1974) http://matt-wand.utsacademics.info/webStatSem/Wedderburn.pdf but I'm not wrapping my head around exactly what quasi-likelihood model is, in particular with ...
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### Difference between Quasi-Poisson and Sandwich Covariance

I understand that both methods can be utilized to obtain correct inference in overdispersed Poisson data. What I don't understand is the difference between them: why the analyst would choose one over ...
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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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### Sampling from under/over-dispersed count data in R

I am currently working a some datasets with count data in R, in which the response is the number of activities of a given type that were performed in one day by a population. For each type, I build ...