A discrete, univariate distribution modelling the number of ${\rm Bernoulli}(p)$ trial successes until a specified number of failures occur.

learn more… | top users | synonyms

1
vote
1answer
50 views

Negative Binomial Distribution R

This question was given in class and I was wondering how to do this in R: "Sixty percent of a large lot of old spark plugs are still usable, and they can be individually tested to determine this. Let ...
2
votes
2answers
32 views

What to call exponentiated coefficients from a Poisson/negative binomial regression of cross-sectional data

In epidemiology, exponentiated coefficients are often reported as odds ratios, relative risks/ incidence rate ratios or hazard ratios. In the analysis of cross-sectional data using Poisson/negative ...
0
votes
0answers
15 views

Creating a jpeg or TIFF for a loop of negative binomial GLMs in R [migrated]

I have made a loop to perform 4 negative binomial GLMs and now I want to graph them in a TIFF. What would be the best way to do this? ...
0
votes
0answers
25 views

R gam() throws error: “Can't correct step size”

I am computing a GAM on a large set of data sets. Almost all of them work, just this one data set makes gam() throw an error. I paste a code that reproduces this error here: ...
3
votes
0answers
110 views

Theoretical justification of choice for confidence interval exact method for the success probability parameter of negative binomial variable?

I have a computer experiment that runs the Bernoulli series with unknown probability $p$ of success. The experiment terminates when $m$ failures are observed. So, the unknown parameter $p$ has the ...
0
votes
0answers
31 views

Relative variable importance for glm & glm.nb - Percentage of deviance explained

I'm currently trying to calculate variable importance for multiple GLMs. I've got both continous and count data response variables. The first I modeled with gaussian GLM, the second with negative ...
0
votes
0answers
24 views

How to make beta coefficients comparable?

My study design delivers both, count data and continous outcomes (e.g., numbers of taxa vs. an diversity index). As these variables are used as response variables, I have to use negative binomial glm ...
1
vote
1answer
35 views

Why does the null deviance in glm.nb differ between models of the same response variable?

I'm struggeling to understand the topic of deviance. Let's have two models as follows: ...
0
votes
0answers
18 views

Negative binomial instead of ordinal logistic?

I have data with one metric and a discrete outcome that I'm trying to estimate the probability of. The outcome is a count, but it's a safe assumption that the increments aren't i.i.d. I thought an ...
1
vote
0answers
46 views

“weight” input in glm.nb function in R. How exactly does the weight affect the likelihood?

I would like to understand how the weight argument of glm.nb is affecting the likelihood function. I understand that glm.nb find the MLE in an alternating iteration process where for a given theta the ...
2
votes
0answers
23 views

Is there a simple model that assumes a negative relationship between the mean and variance of response?

Poisson and Negative Binomial models assume that the variance of response is greater or equal to the mean of response. Is there a simple model where that assumption is reversed, i.e. variance goes ...
0
votes
0answers
21 views

A/B testing zero inflated data

I am trying to conduct an A/B(/C) test to compare the performance of 3 different website pages but I'm facing issues regarding zero inflated data. I have data for each page regarding ...
8
votes
1answer
146 views

Low sample size: LR vs F - test

Some of you might have read this nice paper: O’Hara RB, Kotze DJ (2010) Do not log-transform count data. Methods in Ecology and Evolution 1:118–122. klick. Currently I am comparing negative binomial ...
3
votes
2answers
48 views

Poisson as a limiting case of negative binomial

I was reading "Maximum Likelihood Estimation for the Negative Binomial Dispersion Parameter" by Walter W. Pieogorsch, and in the intro it says the Poisson distribution is a limiting case of negative ...
1
vote
0answers
70 views

Count data in a Structural Equation Model

I am currently trying to fit a structural equation model (SEM) on a certain dataset using R (lavaan package). Some of the most important variables in my model are count data (abundance of different ...
0
votes
1answer
14 views

Why addition of discrete probability values for negative binomial does not match to the cumulative probability value, when it matches in Poisson?

When I manually calculate just negative binomial probability it matches to the value from Excel, or R. formula : (nCr) * (p^k) * ( q^(x-k) ) x = 1000 ; k = 4 ; p = 0.01 ; [ and n = x-1 ; r = ...
0
votes
0answers
27 views

How to interpret logarithmically transformed coefficients in negative binomial regression?

How can I interpret log-transformed independent variables in terms of percent change in a negative binomial regression?
5
votes
0answers
47 views

Is the negative binomial not expressible as in the exponential family if there are 2 unknowns?

I had a homework assignment to express the negative binomial distribution as an exponential family of distributions given that the dispersion parameter was a known constant. This was fairly easy, but ...
2
votes
1answer
67 views

When to use zero-inflated poisson regression and negative binomial distribution

I have a fairly simple dataset looking at the relationship between the first nesting date of a bird in a given year (Date) and the birds overall fledgling production from that year (Fledge; count data ...
3
votes
0answers
40 views

Interpretation of log transformed predictor in negative binomial regression

I mainly want to make sure that I'm making the correct interpretation here. I built a negative binomial regression model predicting a count variable. There was evidence of overdispersion or I would ...
2
votes
1answer
75 views

Quantiles of a compound gamma/negative binomial distribution

Are there any formulae for the quantiles of a compound gamma/negative binomial distribution? That is, suppose we have $$N \sim \text{NegBin}(\alpha, \lambda)$$ and conditional on $N$, $$Y = ...
0
votes
1answer
90 views

Interpretation of $\theta$ in negative binomial regression

First off, a very similar question has been asked before. But the answers to this question did not explain what high/low values of theta mean. Here's my crack at trying to figure out what high/low ...
1
vote
1answer
33 views

Zero-inflated negative binomial model for true zeros

The zeroinfl function in the pscl package in R assumes that zeros include both false zeros and true zeros. I have a zero ...
9
votes
1answer
131 views

Increased Type I error - GLM

Some of you might have read this nice paper: O’Hara RB, Kotze DJ (2010) Do not log-transform count data. Methods in Ecology and Evolution 1:118–122. klick. In my field of research (ecotoxicology) ...
4
votes
1answer
76 views

Generate pseudo-random overdispersed Poisson numbers

I have multiple sets of data which conform to overdispersed Poisson distributions which I can model with the alternative parameterization of a negative binomial distribution ($\mu$ and $D$ instead of ...
3
votes
1answer
81 views

GLM regression - help choosing model specification

I think I need to use a Poisson-family regression or negative binomial regression. My variables are as follows: Y is an integer value ranging from 0 to ~1200. It represents sums (number of species ...
0
votes
1answer
59 views

Validating residual plot count data (different levels)

I am studying the distribution of a marine species using the number of sightings as a dependent variable. When I am trying to validate the plots of the best model I am getting a non-usual pattern, and ...
3
votes
1answer
54 views

Use law of total variance to find unconditional variance of overdispersed Poisson?

First, I need to prove that the distribution of a RV X, where X|lambda ~ Pois(lambda), and lambda ~ gamma(a, B), is a negative binomial. I know that it is, but why negative binomial instead of another ...
0
votes
1answer
25 views

Does and 95% CI containing 1 imply no difference in IRR calculations in the same was as in OR calculations?

My understanding is that with an odds ratio (OR) that a 95% CI containing 1 means that there is no difference between the odds of something occurring between groups. I wondered if this holds true for ...
0
votes
0answers
34 views

Bootstrap of parameter estimates and confidence intervals (hurdle model)

Think you can help with this. I´ve run a set of candidate hurdle models on insect abundance data with pscl package for R. These models had an abundance part with a truncated negative binomial ...
2
votes
2answers
148 views

How to evaluate goodness of fit for negative binomial regression

I'm trying to fit a model estimating waiting time using negative binomial regression, but I'm not sure how to assess the goodness of fit for my model. I would like to compare the negative binomial ...
0
votes
1answer
54 views

correct use of Negative Binomial with a Geometric distribution in a mixed model (glmmPQL)

I am trying to fit a NB GLMM with a gemoetric distribution. I have come across very little information on this form of regression. And would like some pointers/reasurance. some literature is ...
2
votes
0answers
55 views

does the equivalence of Poisson and conditional logit models hold for fixed effect panel data and negative binomial models?

Guimaraes et al. (Rev Econ Stat, 2003, 85/1) describe the conditions under which the results from poisson regression models and conditional logit models are equivalent. I am trying to find out whether ...
2
votes
0answers
63 views

Basic idea of zero inflated two part models(hurdel) and application to big data (machine learning)

I'm currently working on the data which has 90% 0s in response variable. Based on my research, it seems zero inflated models could be a solution to this. However, while I was reading related ...
0
votes
0answers
125 views

R² (squared) from a generalized linear mixed-effects models (GLMM) using a negative binomial distribution

I try to compute the marginal and conditional R² for a GLMM using a negative binomial distribution by following the procedure recommended by Nakagawa & Schielzeth (2013) . Unfortunately, the ...
2
votes
0answers
146 views

Estimating abundance using non-normal count data

I have sample counts of $n=20$ or $n=7$ taken from right-skewed and zero-inflated populations. The challenge in each case is to use the sample to estimate the total count in that population. Each of ...
1
vote
0answers
23 views

Limits of zero-inflated negative binomial in % of zeroes

What are the limits of a zero-inflated regression? Specifically, if more than 80% of the data is zeroes, is the ZINB still valid? What is a good rule of thumb or educated way to understand the right ...
5
votes
1answer
92 views

Zero inflated negative binomial with selection

I am looking for a Stata (or R/Matlab if there's no Stata) implementation of the model described by Greene (1994) (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1293115). It is essentially a ...
0
votes
0answers
11 views

comparing same negative binomial models built with subset data

I have a dataset made our of several stacked datasets (one for each state). I want to check whether a zero inflated negative binomial model with data from an individual state is different from the ...
1
vote
0answers
83 views

GAM with negative binomial and temporal autocorrelation

I want to fit a GAM in R. My data are negative binomial. Plus they have a temporal autocorrelation. ...
0
votes
1answer
33 views

Controlling for variables in negative binomial regression

I would like to run a negative binomial regression, but also controlling for one of the variables. Similar to what you would do with a hierarchical regression. I am trying to predict violence (never, ...
1
vote
0answers
25 views

Leave-one-out cross-validation for negative binomial

I've just started working with the GLM negative binomial and I was wondering if it's possible to carry out a leave-one-out cross-validation for negative binomial GLM as I'm having troubles doing it ...
6
votes
1answer
97 views

In a Poisson process measured with some efficiency, is the measured count still Poisson?

Situation: Say I have a Poisson process, like radioactive decay, producing R particles per second. I measure with a detector. There is a probability P that a particle will be detected by the ...
2
votes
1answer
21 views

I got a good OLS fit for integer variables, do I still need to use count data methods?

First of all, I'm not a statistician, but I'm teaching myself some methods I require for a project I'm doing now. I have a 2D dataset of N observations. For the ith observation, the first entry is ...
1
vote
0answers
48 views

Can i use a mixture model for when I have an omitted variable?

I plan to fit a GAM or GAMM. There is one categorical variable which I think is important for explaining Y (or Y*), but it is not in my dataset - it is measurable but has not been measured. Can I use ...
8
votes
2answers
425 views

Why are Pearson's residuals from a negative binomial regression smaller than those from a poisson regression?

I have these data: set.seed(1) predictor <- rnorm(20) set.seed(1) counts <- c(sample(1:1000, 20)) df <- data.frame(counts, predictor) I ran a poisson ...
0
votes
0answers
91 views

fisher information matrix of Negative Binomial distribution

can anyone show me how to find the Fisher information matrix for negative binomial distribution, if I parameterize the Negative Binomial distribution using parameters mean and size. Thank you!
2
votes
2answers
304 views

Why do ANOVAs and GLM (negative binomial model) give different results for interaction effects?

In my study, children repeat sentences and I count the errors (DV = error rate). Children are divided into two groups (Group factor), and there are two different types of sentences (Condition factor). ...
3
votes
1answer
258 views

Zero-inflation on steroids: choose among Poisson, negative binomial and zero-inflated regressions

I am struggling to fit alternative count models into my data. I guess my problem is just too many zeros. This is my data ...
1
vote
1answer
110 views

Negative binomial distribution fit

I am trying to fit a negative binomial distribution, in R, to my over dispersed data (out of 20 ,14 samples are 0, and rest are less than 5). The mean is $-0.8$ and ...