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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Does the fisher information matrix exist when we can only calculate the quasi-likelihood and not the full-likelihood function?

Does the fisher information matrix exist when we can only calculate the quasi-likelihood and not the full-likelihood function? In GEE, the full-likelihood isn't calculated, but the variances are ...
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May the fisher-scoring algorithm be used when we can only compute the quasi-likelihood?

May the fisher-scoring algorithm be used when we can only compute the quasi-likelihood and can't find the full-likelihood function? IE, some cases where it's advantageous to use GEE rather than Mixed-...
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Why are maximum likelihood estimation methods more robust to missing data than quasi-likelihood estimation methods?

Why are maximum likelihood estimation methods more robust to missing data than quasi-likelihood estimation methods? In GEE versus Mixed-Effects, Mixed-Effects based on ML is more robust to missing ...
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Statistical modeling of count data in different regions

I want to statistically model the likelihood of accident categories in different regions. All accidents happened in the timespan of one year. I think I can demonstrate the problem best by reproducing ...
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Whats the difference between logistic regression and fractional response model? [duplicate]

Can anyone tell me the theory behind fractional response model, how it really works? I wonder if the logistic regression works only with binary variable {0,1}, why when conducting a GLM with ...
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Selecting the reference variable

Let say I want to build a model to predict the number of automobile accidents (based on Driver Age, Region, Area, Power... ) using GLM Poisson (specifically, quasipoisson). For the variable Power: ...
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1answer
28 views

Strange output for pairwise comparisons on glm with quasi-binomial distribution

I'm new to CrossValidated - I've read up on how to ask questions properly but sorry if I do anything slightly wrong. My data is showing whether microplastics were present or absent in the gut of fish ...
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1answer
51 views

“scale” in logistic regression

I am working on translating some R code into Python's statsmodels package, chiefly some logistic regression work that I've done, when I came across the following in ...
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Two intercepts for zero-truncated negative binomial model using VGAM

I am trying to understand the first and second intercept for the zero-truncated negative binomial regression model I estimated using VGAM. Below is my syntax: mod.negb <- vglm(ED_Visit ~ Male + ...
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How can I compare coefficient values from two different count models based on the same variables?

I'm running two quasi-Poisson models with the exact same variables, but on samples from two different countries. I'm doing this because I'm particularly interested in seeing whether the relationship ...
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Validation (optimization) for quasi-poisson regression

I am using GLM Quasi-Poisson for a dataset. I understand that for a Poisson regression, it is possible to use the stepAIC to choose the best model, but this does not apply for Quasi-Poisson regression....
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How to reweigh to get the same effect as mu(1-mu) in quasi glm

The "leafblotch" data shows the percentage leaf area affected by leaf blotch on 10 varieties of barley at nine different sites.(in Faraway) A better variance function is $µ^2 (1 − µ)^2$ and yet this ...
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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 ...
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Quasi-binomial GLM in R

I'd like some advice on data I'm analyzing from a factorial-design study in which each sample is a count of 200 urchin eggs that were exposed to various types and concentrations of pollutants, and for ...
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28 views

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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121 views

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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241 views

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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1answer
85 views

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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131 views

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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1k views

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 ...
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1answer
325 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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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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1answer
458 views

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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210 views

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 ...
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503 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 ...
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157 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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Interpreting Quasi-Linear Regression Predictions

I know that for a simple linear regression the predictions are distributed like: $$y_i\, |\, x_i\, \sim\, \mathcal{N}\big(\widehat{\beta}_0+\widehat{\beta}_1\, x_i,\ \sigma^2\big)$$ $$\text{where: } \...
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Binomial vs. quasi-binomial model

I was trying to fit a GLMM with a binomial distribution (for Yes/No data) in R, and kept running into convergence warnings, which seemed founded given the similar SE's and p-values for the different ...
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74 views

Quasi Likelihood for Gamma and Poisson

I am trying to re-work exact expressions from Generalized Linear Models by McCoullagh and Nelder where Quasi likelihood is stated as: $$ Q(\mu,y)= \int _{y}^{\mu} \frac{y-t}{\sigma^2 V(t)} dt$$ In ...
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Interpreting Odds Ratio for a Continuous Variable in a Fractional Logit Model

I am using proportion to data to measure the effect of several variables on on lameness level (continuous variable, given as % of herd lame). Ideally I would have the counts to model it as over-...
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1answer
111 views

Residual deviance and interactions in quasi-poisson model and negative binomial model?

My data was over-dispersed (dispersion coefficient over 5), so I have fitted both the quasi-poisson model and the negative binomial model. I notice that the regression coefficients are almost the same,...
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151 views

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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1answer
177 views

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. ...
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1answer
3k 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 ...
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152 views

How to compare nested quasi poisson models?

I have the following nested quasi poisson models for overdispersed count data: ...
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113 views

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 ...
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542 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-...
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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 ...