Questions tagged [quasi-likelihood]
In GLMs, quasi-likelihood estimation is a way to allow over- or under-dispersion by choosing an appropriate variance function.
130
questions
0
votes
0answers
5 views
Do we have to check heteroscedasticity when estimating quasibinomial model?
As we all know we are not interested in checking heteroscedasticity when estimating logit model when our variable is binary (taking only values 0 and 1). However do we also don't have to test ...
0
votes
0answers
9 views
Is there any sense of applying cross validation to quasibinomial variable?
We can apply without any doubts cross validation for linear models as well as for binary models.
For linear models for example we can output RMSE and MAE and Accuracy for binary models.
But I have ...
0
votes
0answers
13 views
GLM on features selected by PLS-DA
First question: Can we use GLM on specific variables selected by PLS-DA latent components? To obtein p-value of response of prediction (e.g.for each selecetd variable of comp 1)?
Second question: What ...
0
votes
1answer
51 views
Comparing model efficiency
I hope you all don't mind me asking this question.
I have two models :
general linear mixed effects model
...
4
votes
0answers
82 views
Why is my quasibinomial GLM estimator biased - Monte Carlo simulation
I'm playing with some Monte Carlo simulations to get an idea of the properties of some linear and non-linear models.
The linear OLS model in my case is specified as:
$Y_t = \beta_0 + \beta_1x+ \...
1
vote
0answers
21 views
If my data is overdispersed when binomial but not when Poisson does that alone mean I should use Poisson GLM?
Top is Poisson, bottom is Binomial
Source of variation is different people observed over a week long period. 118 people to be exact.
want to make predictions about the proportion of days/week one ...
1
vote
0answers
31 views
Avoiding data dredging with quasibinomial GLMs
Because my data is a bit overdispersed I am using quasibinomial GLM to analyse them however this means I cannot use AICs to compare my models.
I am therefore using drop1 and update functions to do ...
0
votes
0answers
20 views
Should “kenward-roger”/“satterthwaite” degrees of freedom be used with “GLMM” fitted with penalized quasilikelihood"? [duplicate]
Should kenward-roger degrees of freedom correction be used with generalized linear mixed models fitted with penalized quasilikelihood/"PQL"?
There's no reason it isn't implementable but the ...
1
vote
0answers
30 views
Equivalence between prediction interval and hypothesis test on Poisson predicted values with 2/3 power transformation
I fitted a quasi-Poisson regression model to predict the value of a certain counting variable.
Once the predicted value $\widehat{\mu_{0}}$ was obtained, I calculated the upper limit of its prediction ...
1
vote
0answers
21 views
Overdispersion in logistic model
I'm relatively a newbie in R, and I've been trying to make a silly example of logistic regression to predict, according to Age and Sex whether someone dies of corona or not. I'm from Colombia, so my ...
0
votes
1answer
100 views
Interpreting coefficents on quasibinomial model
This might seem like a pretty basic question but I've scoured seemingly everywhere and can't get a definitive answer.
I have a response parameter "rr" bound by 0-1 which is essentially the ...
0
votes
1answer
45 views
2
votes
1answer
76 views
Quasi Poisson vs Negative Binomial [duplicate]
I read in several sources that the Quasi Poisson model and the Negative Binomial, should produce (on average) the same results. I tried a simple example and, although very close to each other, the ...
0
votes
0answers
24 views
R binomial model where DV is a proportion and distribution appears Bimodal
I've been attempting to fit a binomial model to a data set of 1,000,000 accounts where the DV (rr) is a percentage of account balance that been has paid (EX account with total owed of 100 dollars has ...
0
votes
0answers
20 views
Why can we use a deviance test for a quasi likelihood models?
I know that the reason why you don't obtain a value for AIC if you fit a quasi-likelihood model is because a quasi-likelihood is not a real likelihood, and to obtain a value for AIC, you need a real ...
1
vote
0answers
79 views
Testing for endogeneity in a negative binomial model
I'm trying to fit a negative binomial model to my data because the dependent variable exhibits overdispersion. However, one of my reviewers is insisting that I also test for endogeneity. He or she is ...
0
votes
1answer
64 views
Interpreting interaction among 2 categorical IV in quasi-poisson regression
In my dataset, I'm looking at the impacts of developmental and immune phenotypes on morbidity- specifically, I want to determine if developmental phenotype has an effect on the difference in morbidity ...
1
vote
0answers
20 views
Can we mix conlcusions from Poisson and Quasi-Poisson?
Currently I'm working with ecological studies, where my response is a count variable. I need to estimate several models, each one represents a city. Afterwards I aggregate them to obtain meta-analysis ...
0
votes
0answers
19 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 ...
1
vote
0answers
10 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
14 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
10 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
70 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
8 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
36 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
0answers
24 views
How to interpret coefficients off a quasi-poisson regression with weights and a fixed effect?
My Quartile variable is a binary variable, 0 = lowest quartile, 1 = highest quartile. My variable being explained cases_100k is ...
1
vote
1answer
98 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
36 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
16 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
18 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
20 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 $µ^2
(1 − µ)^2$ and yet this ...
2
votes
1answer
78 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
231 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
37 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
169 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
120 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
604 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
25 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
210 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
487 views
Quasi-Poisson - Goodness of fit
Can I use likelihood ratio chi-squared tests to test the goodness of fit for quasi-Poisson models?
3
votes
1answer
119 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
182 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
71 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
86 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
344 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
74 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 ...
2
votes
0answers
200 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
2k 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
408 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
54 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 ...
