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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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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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39 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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1answer
94 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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84 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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48 views

Quasipoisson model variable selection and find best model

I am running a Quasipoisson model in R with a lot of variables. This is my outcome: I want to find out which variables have an influence on the dormouse abundance (number of nests). After doing the ...
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1answer
93 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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124 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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199 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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How to interpret log-likelihood score as compared to mse

Say one has a linear dynamic system as follows: $x_k = Fx_{k-1} + v_k$ $y_k = Hx_{k-1} + w_k$ with $v \sim (0, Q)$ and $w \sim (0, R)$. I am estimating $(x)_k$ using a normal Kalman Filter and ...
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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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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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169 views

What are the regularity conditions for quasi-maximum likelihood estimators?

What are the regularity conditions for Quasi-Maximum Likelihood Estimators (QMLE)?1 Could you advise me a good book where I can find detailed proofs? 1. For example, regularity conditions for MLE ...
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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. quasibinomial 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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How to analyse overdispersed and autocorrelated data from a repeated measures design experiment (Mixed model time-series analysis)?

I have survival (number of alive+number of dead=total number) and productivity (total brood number) data on two ant species made to corxist in lab for 60 days. The question of interest is if group ...
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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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383 views

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
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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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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
112 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
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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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119 views

How to compare nested quasi poisson models?

I have the following nested quasi poisson models for overdispersed count data: ...
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73 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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1answer
377 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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327 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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2answers
785 views

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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278 views

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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2answers
174 views

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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1answer
330 views

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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156 views

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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1answer
278 views

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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1answer
1k views

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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1answer
270 views

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 ...
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1answer
437 views

QMLE with normal distribution for GARCH - consistent & asymptoticaly normal? (R, fGarch)

In the section Details of the documentation of the garchFit function we find the following statemement: "QMLE stands for Quasi-Maximum Likelihood Estimation, ...
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P values from quasi-binomial generalised linear models in R

I am sorry if this has been asked elsewhere. I could find it when looking. I am running a quasi-binomial generalised linear model (to correct for overdispersion) weighted by a count of sample size. I ...
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256 views

How to describe this model in glmmPQL in R with mathematical notation?

Problem I am learning general linear mixed models and have a lot to still learn. Consider the following model, using glmmPQL I have the following model: ...
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1answer
2k views

What is the difference between beta regression and quasi glm with variance = $\mu(1-\mu)$?

First let me give some background; I will summarize my questions at the end. The Beta distribution, parameterized by its mean $\mu$ and $\phi$, has $\operatorname{Var}(Y) = \operatorname{V}(\mu)/(\...
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compare quasi-Poisson models with different response variables

My response variable is a combining of two variables which I would like to rescale with different weights. ratio:0.5/0.5; 0.25/0.75; 0.1/0.9. Now I would like to test which is the best fit. The three ...
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1answer
341 views

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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1answer
735 views

How to measure goodness of fit for a poisson regression model?

I am working on a poisson regression model in sas. But I am not able to determine how good the fit is. I have used PROC GENMOD, PROC NLMIXED, PROC GLIMMIX and now I want to compare the results. How ...
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9k views

What is the difference between logistic regression and Fractional response regression?

As far as I know, the difference between logistic model and fractional response model (frm) is that the dependent variable (Y) in which frm is [0,1], but logistic is {0, 1}. Further, frm uses the ...