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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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Fit Bi-variate Poisson Regressopm in Python [on hold]

Is is possible to do bivariate poisson regression in python? The glm function in scipy only seems to mention the univariate case are they any other packages that could help? Thanks Baz
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Population Mean Estimation using Poisson Regression?

Assuming I have count data, where x is the counted variable of interest and y is some total number of counts for a given sample. Then, can I improve my estimate of $\mu_x$ by using a Poisson ...
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Modeling rates with complex survey data

I have data derived from a stratified, cluster random sample with post-stratification weights (corresponding to the inverse probability of response). These are person-level data. Let's say I want to ...
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1answer
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count data and Poisson GLMs to predict monetary amounts

I have to predict money amounts, which are always greater than 0. The distribution is very tailed (i.e. there are many small values but also many large data). Just wondering would a count data model ...
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How to write the Poisson regression formula in both proper and understandable way for animals scientists

I analyze the following count data gathered in a contingency table using Poisson regression with the formula given below. Category Group C1 C2 C3 I 4 1 3 II ...
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22 views

Poisson regression on a contingency table - overdispersion

I have the following contingency table Category Group C1 C2 C3 C4 C5 Total I 4 1 3 9 9 26 II 7 7 1 6 5 26 III 0 2 1 1 13 17 IV ...
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2answers
60 views

Can I use rates (e.g., crude rates) as an explanatory variable (covariate) in regression?

I am working on formulating a regression problem where my dependent variable is the number of heart disease cases (count data) in a population. I am looking to do an ecological poisson-based ...
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Long tailed count probability distributions - with available likelihood calculation and random number generation

in real world data, often (always to me) happen that for modelling count data Poisson Negative binomial multinomial dirichlet-multinomial probability distribution, are not robust to outliers, as ...
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16 views

How to interpret Negative Binomial results with dummy variables

I am working on a model that looks at how the four seasons of the year have an impact in the count of crimes from 2008 to 2017. To run the model we grouped the data by season for each year and ...
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can i use random forest for feature selection and then use poisson regression for model fitting? [duplicate]

Variables that are important in random forest don't necessarily have any sort of relationship with the outcome. So would it be wise to use random forest to gather the most important features and then ...
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Plotting fitted lines for two different groups from a glm model with an interaction [migrated]

I have the following model: mod <- glm(data=data, events ~ treatment * size, family = quasipoisson) With the following output: ...
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38 views

Why does a summary() call on a GLM model give me T rather than F values?

I constructed a quasi-Poisson model ("mod") with two predictors and an interaction term using glm(). One of the predictors is ...
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51 views

How to compare model fit of OLS and Poisson regression?

I have built two regression models 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. One is a ...
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28 views

How to use Poisson regression in R to determine if two different years of count data are statistically significantly different?

I’ve been asked to determine whether two years of count data (before intervention and after intervention) are significantly different using Poisson regression in R. Any suggestions?
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ANOVA tables are exactly the same using OLS and Poisson regression?

I have fit two models in R, as follows: m1 = lm(y ~ x * z) m2 = glm(y ~ x * z, family="poisson") Where y is the dependent ...
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1answer
101 views

Zero-inflated count data simulation in R

I need to simulate data to explore the question: "What happens if my data have zero inflation, but I ignore it and fit a standard count model instead?" More specifically, how can I add zeros in a way ...
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2answers
61 views

Negative estimated means in Poisson regression output

Can anybody help me understand what the negative estimated means in a Poisson regression mean? I am trying to model the count of damages during transit for 4 different logistics providers. To make it ...
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1answer
35 views

Time taken to receive funding: A count variable?

I am trying to predict time taken by a startup to receive funding based on specific characteristics of entrepreneur. Regardless of a bunch of transformations and ...
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How can I (visually) diagnose the residuals of a quasipoisson regression?

I've estimated a quasi-Poisson regression in R - a choice I've largely opted for because it "makes sense" for my application - I'm looking at count data with lots of zeros in the response variable, ...
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1answer
74 views

How to interpret Zero-Inflated Poisson in WINBUGS?

I have Winbugs code for a zero-inflated Poisson (ZIP) model. I obtained this code from my lab at university and the person who wrote it is not accessible for me to ask questions. Here is the code: <...
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VGAM plotting predictions and confidence intervals genpoisson/generalized poisson

I fit a generalized poisson using VGAM and can output predictions using predict. However, the fitted.values are a matrix, ...
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Is there a option to find studentized deleted residual for Zero Inflated Poisson model?

I have fitted my 3x3 contingency table with Zero Inflated Poisson model. I am trying to explore all types of residuals associated with the model. There is no option to calculate studentized residuals. ...
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1answer
43 views

Deviance residuals in Poisson GLM

I am learning the concept of residuals in modelling. I performed a Poisson GLM for a 3x3 contingency table and I got the summary of the model. My question is: the deviance residual from the in-built ...
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1answer
29 views

Is Poisson model in R appropriate here?

I am graphing a dataset in R that is whether a customer makes an unplanned purchase which can take values from 0-infinity, against customer path length which may take only positive values. The ...
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Use of cross-sectional survey weights when employing mixed-effect regression models using multiple cross-sectional data

I have conducted an analysis using multiple-cross sectional datasets (i.e. combining different waves of the same survey). The survey provides cross-sectional weights for each wave. Considering that a ...
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Negative binomial model vs zero-inflated negative binomial - theoretical justifications

I have a count variable that I would like to predict using a categorical variable (it has 4 levels). I would like to decide whether I should use Poisson, negative binomial, or zero-inflated negative ...
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1answer
58 views

Adding a predictor reduce R squared

Currently, I am doing Poisson models with N=16,000. My study requires me to find $R^2$ for each model (using 'rsq' package). When I add P12, the $R^2$ decreased as shown below. ...
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Zero-inflated Poisson with clustered data in R

I am working with ecology count data with a significant amount of zeroes, and I used a multivariate zero-inflated Poisson regression to evaluate the impact of two independent variables on my dependent ...
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1answer
47 views

Poisson assumptions

I am dealing with a dependent variable that is either 0, 1 or 2 in theory it is unbounded and it can take values more than 2 so I am motivated to test Poisson model first. The frequency counts for ...
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1answer
23 views

Can covariance parameter estimates and fixed effect estimates of a Generalized Linear Mixed Model be correlated?

Specifically, I am fitting a Poisson Mixed Model with random coefficients and intercepts on longitudinal count data. The rate ratio between time points is log normal with expected value $e^{\mu}e^{\...
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Poisson data prediction with month variable

I have count data based on the number of failures per month. I have month, year, type of failure, and the count of fails. I have tried using R's glm package, ...
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Performance of a product on an unknown location

The problem I aim to solve is to find the right location in a shop for each product. The shop is a retailshop, not a supermarket. I have 32 weeks of salesdata of products on shelves within a store (...
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69 views

Mixed models for zero-inflated count data in R?

I have a dataset containing scores on a measure of uncommon experiences. The scores are derived as a count of the number of items rated as present divided by the number of items that were answered by ...
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121 views

Interpreting summary output from poisson glm in R

I'm learning about regression at university and I really strugging to find good sources to understand the process of model analysis. I have this model below. I'm trying to make the best fitting model ...
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1answer
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How Can Poisson Regression Predict a Count of Zero? [duplicate]

I am reading "Modelling Count Data" by Hilbe and I feel I am missing something fundamental about Poisson Regression. $\hat{\mu} = \exp(\alpha + \sum\beta_ix_i)$ One of the requirements for using it ...
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XGBoost Poisson Objective Function When Data is Over-dispersed [closed]

I am modeling very over-dispersed count data with the goal of prediction. The data is not zero inflated (there are no zeros), but there are a lot of values of 1. ...
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Residuals in Poisson regression in R [duplicate]

While performing Poisson regression in R, I realized that the residuals, as given in the object slot (model$residuals), differ both from the values returned by the <...
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How do you format R code to include multiple offsets in a Poisson regression?

I am running a Poisson regression with a negative binomial distribution using package MASS in R. I'm looking at which climate ...
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1answer
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People count regression analysis

I have data of number of customers entering a business and number of customers leaving a business every 15 minutes. I need to predict the number of people that will be entering and leaving the ...
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Poisson processes or regression for lambda

I am asked to solve a problem where I have a machine producing toys in tho slots and want to predict number of faulty toys. The data is like this: ...
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Interpretation of poisson regression parameter for one categorical predictor

I have the following results from a logistic regression with one categorical predictor with two levels, fitted using the phyloglm() function from the ...
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2answers
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Validity of non independent observations in Poisson regression when modelling incidence rates

I have a question regarding fitting a poisson model to non-independent observations when assessing the incidence of an outcome. I am interested in the incidence of some outcome, O, over time. I have ...
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2answers
74 views

Regression assumption

I have a data set where $100$ people made $500$ trips for $5$ days. I want to build a trip-level regression (zero-inflated Poisson) where the dependent variable will be the count of hard-braking in ...
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Can I model incidence per 1000 people per month using poisson regression without an offset or using weights?

I am trying to model incidence rates (number of malaria cases per 1000 people per month) over time. I only have the data in the form of rates per 1000 people per month, i.e. I do not know the total ...
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1answer
54 views

Different results from poisson glmer and glmmadmb when using emmeans (lsmeans)

Why would I be getting drastically different results from glmer and glmmadmbM for the same model when using emmeans? The results from summary() are the same. EMmeans glmer: ...
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Model and predictor selection in generalized linear models

I’m analyzing count data in R and I want to make two decisions: 1) what type of regression to use (Poisson, negative binomial, zero-inflated, etc), and 2) what predictors to include in the model. I’m ...
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2answers
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How do we compare count models for prediction and inference?

I have estimated a number of count models on a data, including Poisson, Zero-Inflated Poisson (ZIP), mixed-effects Poisson, mixed-effects ZIP and, a few different versions of each of these based on ...
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Autoregressive mixed effects generalised linear model for zero-inflated count data

I have a multi task learning scenario where I have $I$ items in each of $J$ groups and for each item $i$ I have $T_i$ observations $\{y_{i,j,t}\}_{t=1}^{T_i}$ (which are non-negative and mostly zero). ...
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Constraining predicted probabilities of a poisson regression with a binary outcome [duplicate]

I have used a modified poisson regression to model a binary outcome because I want to obtain relative risks, rather than odds ratios. The predicted probabilities are of interest, but many are beyond ...