Questions tagged [quasi-likelihood]
In GLMs, quasi-likelihood estimation is a way to allow over- or under-dispersion by choosing an appropriate variance function.
111
questions
0
votes
0answers
10 views
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 ...
0
votes
0answers
3 views
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-...
0
votes
0answers
7 views
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 ...
0
votes
0answers
5 views
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 ...
1
vote
1answer
17 views
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 ...
0
votes
0answers
7 views
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: ...
1
vote
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 ...
0
votes
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 ...
0
votes
0answers
16 views
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 + ...
0
votes
0answers
11 views
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 ...
0
votes
0answers
15 views
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....
0
votes
0answers
15 views
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 $Āµ^2
(1 ā Āµ)^2$ and yet this ...
2
votes
1answer
38 views
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 ...
1
vote
0answers
57 views
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 ...
1
vote
0answers
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, ...
3
votes
1answer
73 views
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 ...
2
votes
1answer
42 views
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?
2
votes
2answers
215 views
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 ...
1
vote
1answer
20 views
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 ...
0
votes
1answer
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 ...
1
vote
1answer
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?
2
votes
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 ...
1
vote
0answers
98 views
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).
<...
1
vote
1answer
62 views
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 ...
1
vote
0answers
59 views
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.
...
3
votes
1answer
196 views
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 ...
2
votes
0answers
48 views
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 ...
1
vote
0answers
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....
1
vote
1answer
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 ...
1
vote
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:
...
1
vote
0answers
36 views
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 ...
15
votes
2answers
934 views
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 ...
1
vote
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 ...
0
votes
0answers
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?
1
vote
0answers
37 views
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 ...
2
votes
0answers
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 ...
1
vote
0answers
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 ...
0
votes
0answers
135 views
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 ...
1
vote
0answers
84 views
Interpreting Quasi-Linear Regression Predictions
I know that for a simple linear regression the predictions are distributed like:
$$y_i\, |\, x_i\, \sim\, \mathcal{N}\big(\widehat{\beta}_0+\widehat{\beta}_1\, x_i,\ \sigma^2\big)$$
$$\text{where: } \...
4
votes
2answers
457 views
Binomial vs. quasi-binomial model
I was trying to fit a GLMM with a binomial distribution (for Yes/No data) in R, and kept running into convergence warnings, which seemed founded given the similar SE's and p-values for the different ...
1
vote
0answers
74 views
Quasi Likelihood for Gamma and Poisson
I am trying to re-work exact expressions from Generalized Linear Models by McCoullagh and Nelder where Quasi likelihood is stated as:
$$ Q(\mu,y)= \int _{y}^{\mu} \frac{y-t}{\sigma^2 V(t)} dt$$
In ...
0
votes
0answers
576 views
Interpreting Odds Ratio for a Continuous Variable in a Fractional Logit Model
I am using proportion to data to measure the effect of several variables on on lameness level (continuous variable, given as % of herd lame). Ideally I would have the counts to model it as over-...
1
vote
1answer
111 views
Residual deviance and interactions in quasi-poisson model and negative binomial model?
My data was over-dispersed (dispersion coefficient over 5), so I have fitted both the quasi-poisson model and the negative binomial model. I notice that the regression coefficients are almost the same,...
3
votes
0answers
151 views
Quasi Poisson model quasi-likelihood
Wikipedia (https://en.wikipedia.org/wiki/Variance_function) gives this as the general definition of quasi-(log)-likelihood:
$$ Q(\mu,y)= \int _{y}^{\mu} \frac{y-t}{\sigma^2 V(t)} dt$$
In the case of ...
1
vote
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. ...
3
votes
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 ...
1
vote
0answers
152 views
How to compare nested quasi poisson models?
I have the following nested quasi poisson models for overdispersed count data:
...
0
votes
0answers
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 ...
3
votes
1answer
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-...
1
vote
0answers
632 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 ...
