Questions tagged [poisson-regression]

Poisson regression is one of a number of regression models for dependent variables that are counts (non-negative integers). A more general model is negative binomial regression. Both have numerous variants.

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How to deal with treatment variable which is determined by the outcome variable

I have a categorical treatment variable, MessageType, that has 12 different values. The outcome variable, Crash, sometimes determines these values. So more crashes lead to certain types of messages, ...
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validation of Negative binomial regression for count data

My response variable has the following distribution. I intended to find the effects of explanatory variables on the number of infections. Since Infection is a count data with Mean: 14925, Variance: ...
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How does one decides if conditional Poisson response is valid for count data?

Suppose I have count data grouped in equal time intervals as a dependent variable. Often a Poisson regression is a better suited GLM model then, say, conditional Gaussian. Due to my little training ...
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Dealing with zeros in the dependent variable generated by separate processes

There are several questions here on dealing with zero inflated dependent variables, but my question is slightly different than them. I am working with a continuous/count dependent variable. The ...
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Hierarchical Poisson regression with a normal group level

I have a group of items. I model each with the Poisson regression (counts), but one of the regression coefficients is modelled on a group level using the normal distribution. Assuming there are $m$ ...
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Weighted least squares using poisson regression link-log example in R

I'm studying about “weighted least squares” using poisson regression example. And I could got it for poisson link “identity” as following. it would be well because of the same one to minimize (or ...
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Risk ratio for multiple outcomes model

I was wondering if there is a way to calculate risk ratios for multiple outcomes in R using a modeling approach that allows me to adjust for confounders. Using a binary outcome approach, I have: <...
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Panel data or separate regression models on count data

I'm trying to find the best way to model count data collected over three years. I have data representing the number of complaints pre-schools in one city has received for the years 2017, 2018 and 2019....
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How to treat negative confidence intervals in response scale for GLMM Poisson/negative binomial distribution

I am using emmeans to produce estimated marginal means from GLMMs with Poisson or negative binomial distributions, but for a few of my models the confidence interval is in the negative. I was not ...
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GLMM estimation of unconditioned means

I'm trying to get estimates of overall means, not conditioned on a factor, from my data in a multilevel model with negative binomial or Poisson distribution. In other words, taking into account my ...
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Poisson Regression for binary outcomes - why is legitimate?

I have learned - and taught - that to build a regression model for a binary outcome one should use a logistic regression, for a outcome that has discrete counts one should use the Poisson regression, ...
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Poisson/NegBinom model with identity or logarithmic link

I am wondering about the implications of using (1) a logarithmic, and (2) an identity link within a Poisson model for count data. I have read through related posts here on CV: Pros and Cons of Log ...
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Model Selection- Poisson and Negative Binomial

I have run 2 GLM models with same variable specifications except that one was run with the response variable following a generalized poisson distribution and the other with a negative binomial ...
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is there a difference in fitted values $\mu_i$ depending on the link function chosen for a poisson GLM

I'm new to stats/R, and have just started learning about generalized linear models and am a little lost. Does choice of link function (in this case identity link vs log link) affect the fitted values $...
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Fitting a Poisson Model

In GLM we cannot find an explicit solution for the likelihood equations and we are forced to use iterative methods like Newton-Raphson or Fisher-Scoring. However, in the Poisson model we have ...
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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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Appropriate model for count data when response variable minimum value is far above zero

CASE 1: I am trying to model count data; the response variable, y=c(12, 15, 34, 13, 12, 33,....,45) while the explanatory variables are location (binary, rural/urban), marital status, education level, ...
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Poisson vs table for calculating risk ratio differences

When I use tabodds to calculate odds ratios and use logistic regression, I get the same result for a univariate model. However, when I calculate risk ratios using tabodds and using Poisson regression, ...
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When theta.ml result will be very deviate from the true theta?

theta.ml using fitted mu from glm.nb() and glm(family = poisson) are all very similar to ...
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Zero inflated generalized poisson: How can I compare 2 models?

I'm using Statsmodels to fit count data by Zero Inflated Poisson Regression. Suppose that I have 2 models: ...
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Can we associate poisson regression coefficients with the degree of harmfulness to the outcome?

I tried a poisson regression model with the count of death as outcome and number of mutations in 4 different genes (G, O, S and V) as predictors and log (number of cases) as offset. The following is ...
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Misleading Estimated Marginal Means in Zero-inflated Mixed Models

I am working with a number of zero-inflated poisson and nbinomial mixed models (with an offset), but when I produce estimated marginal means from them they are sometimes many orders of magnitude ...
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Distribution when count data have no zero counts

My data are monthly counts of samples submitted to a laboratory for testing. The data have no monthly counts of 0 for when no samples were submitted. How should this data be handled? What distribution/...
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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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Counting samples seem to not be Poisson distributed, need sanity check

I have an exercise where I have to use Poisson one-way classification / Regression of some data. The data I have is a set of 120 samples grouped the following labels A, B, C, D, E, and F. For each ...
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Resurrecting coefficients from simulated data in Poisson regression

I am trying to understand the model estimates of poisson regressions. There are other posts on this topic on StackExchange (How to interpret coefficients in a Poisson regression?, How to interpret ...
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Understanding the assumptions of a Poisson regression model? Modeling plant diversity

I have data on plant diversity in response to a fully crossed treatments of fertilizer and light in grassland systems: ...
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Correct interpretation of estimates in poisson regression output

I am learning to use and validate the Poisson regression model and interpret the results. I am using some data on grassland plant diversity in response to fertilizer and light. The experimental design ...
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Poisson Rate Regression [duplicate]

I don't get the purpose of poisson rate regression. How do we know if it’s better to use poisson or poisson rate regression in a given situation?
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Poisson Regression to estimate mortality

I am trying to estimate the rate of death for patients with epilepsy over a period of 13 years. I have the number of people known epileptics who died and all causes of death. This is the way I have ...
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Independent Poisson variables in comparison of confusion matrices

I want to compare two confusion matrices, as I discuss here, and I have realized that examining the accuracy of each model is inadequate for such a comparison. Gung gave a nice answer about how to ...
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Why use an offset variable as a predictor instead of just converting outcome to a rate?

I am reporting the results of an analysis where we tested the effect of various demographic predictors on the number of counselling sessions undertaken by participants during a clinical trial. I ran a ...
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Poisson regression comparison of two confusion matrices

I have two groups, say $\text{G}$ and $\text{H}$, and they each perform a classification task. Let's say that I get the following confusion matrices $$\text{G's Performance}\\ \begin{array} &&...
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How to use quasi-Poisson model after overdisperson with glmer(mydata,family = poisson(link = 'log'))?

I have to fit my data with Laplace glmm with random effect using poisson distribution error. ...
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Why output differs between R and SPSS when run Poisson glm on count data

I am puzzled why output differs between R and SPSS when run Poisson glm on count data? While R returns Estimate, Standard Error, z value and Pr(>|z|), SPSS does Walt χ2, df and significance.
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Count Poisson Regression dataset [closed]

I'm looking for a dataset which I can use in R modelled by the glm function with family=poisson. I need there to be at least four predictor variables and the count has to be the explanatory variable.
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beta binomial to reduce overdispersion for binomial data (zero inflation)

I know that a negative binomial model is often use to solve the problem of overdispersion in count data (poisson regression). Now, someone said that a beta binomial model can also be used to solve the ...
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Interpretation of zero-truncated Poisson regression coefficients

For non-truncated Poisson regression, a count random variable $Y$ is assumed to follow a Poisson distribution $$ \mathbb{P} \left\lbrack Y = k \right\rbrack = e^{-\mu} \frac{\mu^k}{k!}, \; k = 0, 1, 2\...
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Bayesian model: binomial(s) conditional on Poisson

I am struggling a little with how to conceptualize and specify a particular Bayesian model. I suspect the solution is rather simple but for some reason I am having a hard time thinking about this. ...
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How to account for adjustment of categorical parameters in R poisson regression

I recently read about poisson regression in R and also about the offset that can be set, as for example described here. If I understand correctly, this can be used to account for continuous ...
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GEE: results significant only with independent and exchangeable correlation structure

I am trying to fit a GEE Poisson model on a panel dataset consisting of T=360 and N=304 for a total of >108,000 observations in Stata. My response variable measures a count of people imprisoned, and I ...
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R : create a glm with quadratic terms [closed]

I try a model on whatsoever data : $ \ln \mu_i = \beta_0 + \beta_1 t_i $ and then compare it with : $ \ln \mu_i = \beta_0 + \beta_1 t_i + \beta_2 t_i^2$ For the first model I wrote in R : ...
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predicted counts for Zero-Inflated Poisson model differ from original samples

While experimenting with statsmodels' Zero-Inflated Poisson count model using artificially generated data, I noticed that although the parameters used to generate the data for fitting were ...
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Specification of AR1 correlation structure for multilevel zero-inflated Poisson model with sparse outcome

I am trying to specify an AR1 correlation structure for a multilevel zero-inflated Poisson model using glmmTMB. A sample of the ...
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Estimating distribution parameter (lambda) from a poisson regression

I'm running a glmm model with a poisson $(Poi\sim (\lambda))$ family. Now I need the value of the $\lambda$ to make the PIT histogram. I know that $\lambda = \exp\{...
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Does one need to run a poisson regression to estimate the scale parameter before using negative binomial regression?

The negative binomial has two parameter in its distribution. Neg bin has a scale and a probability parameter. I’d imagine the scale parameter estimated in poisson regression is only one of them. Does ...
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How to compute the prediction interval with poisson GEE?

How do I compute using R the prediction interval for poisson GEE via geeglm? I prefer geeglm because of the "waves" argument. Aside: I'm trying to predict what future counts of an event will be in ...
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May “offsets” be used in mixed-effects poisson regression?

Mixed-effects poisson regression studies counts for example of the incidence of a disease given the individual's random-intercept/slope. Mixed-effects regression studies individuals rather than ...
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May “offset” in poisson regression be a constant? [closed]

Let's say I'm trying to model the incidence of disease per 1000 people. I have data where people are id'd for example 1001,1002,1003,.... am I allowed to specify the offset as ...
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Does poisson GEE require “scale.fix=TRUE” and “scale.value=1”?

Does poisson gee require scale.fix=TRUE and scale.value=1 for the package geepack? Aside: I heard gee package in r doesn't allow one to specify the scale.value and only let's one specify scale.fix=T/...

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