# 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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### Calculate weight for GLM-quasi poisson model

I am running several models with the quasi-Poisson family. I am looking at data from vulture restaurants. Vulture count was modelled at each site as a function of either a linear or quadratic effect ...
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### How to derive standard errors of regressors in quasi-Poisson regression?

Suppose I want to relax a Poisson Regression to allow for overdispersion and apply a quasi-likelihood approach: $$E[y_i|x_i] = exp(x_i^T \beta)$$ $$Var[y_i|x_i] = \phi \cdot \mu_i$$ In other words, ...
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### Diagnostics and assumptions for Fractional Logistic Regression

I am investigating the effects of Variable X on Y'. Y' is a bounded, non-negative integer. So I have divided Y' by its upper bound to obtain a fraction Y which is in [0,1]. I am following a study ...
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### The statistical model equivalent to this R formula

This is how my model is written in R: glm(formula = prop ~ A * B * C * D , family = quasibinomial, data = data, weights = w) This is a quasibinomial ...
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### Overdispersion Mixed generalized linear model

I am running a mixed generalized linear model to analyze insect capture in a baited trap. The experiment consisted of 3 separate cages, in each one one treatment (C+, C- or T) and 10 insects were ...
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### Given that quasibinomial regression models extra-binomial variation, why ever do binomial regression if quasibinomial is more flexible?

In reading about quasibinomial regression: The quasi-binomial distribution, while similar to the binomial distribution, has an extra parameter 𝜙 (limited to |𝜙|≤min{𝑝/𝑛,(1−𝑝)/𝑛} ) that attempts ...
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### Coefficients in quasibinomial regression and model prediction plots

I'm using two quasibinomial models. In the first model, the dependent variable is the proportion x of successes in experiment A. In the second model, the dependent variable is the proportion y of ...
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### Profile (quasi-)likelihood score tests

Suppose I have a log-likelihood or quasi-log-likelihood for my data in terms of the parameter vectors $\theta$ and $\psi$: $$L(\theta;\psi)=\frac{1}{T}\sum_{t=1}^T{\log{f(y_t|\theta;\psi)}}.$$ (I am ...
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1 vote
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### Can I use quasi-binomial regression on proportion data in this way?

prop.pass = proportion of students who passed the exam num = number of students who sat the exam ...
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### Poisson fixed effects model in pglm estimating time-invariant predictor

I am analyzing panel data on various municipalities (id_mun) over several years using the pglm package in R. My dataset contains a variable "treatment" which is continuous but is time-...
69 views

### Quasibinomial GLMM with LASSO regularization in R

I am currently assessing drivers of deforestation using a GLM (generalised linear model) with LASSO regularization (using package glmnet in R). As the response variable is % of area deforested I have ...
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### Does binomial regression weight some observations more heavily than other?

I am performing a quasibinomial regression, where each subject has an unfixed number of trials. So one subject may have had 5 trials while another had 90. In R the regression equation follows: glm(...
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