A discrete random variable $X$ has a negative binomial distribution, indexed by parameters $p \in (0,1)$ and $r \in \mathbb{Z}$ if its probability mass function is $$ \Pr(X = k) = {k+r-1 \choose k} (1-p)^r p^k $$ The negative binomial distribution is interpreted as the number of ${\rm ...
2
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0answers
16 views
Non-integer dependent variable in negative binomial models
I have non-nested count data that I've interpolated from one area to another based on the proportion of the area that lays in each. This is ZIP codes to counties, so most nest cleanly, with a few ...
1
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1answer
50 views
Fitting a Poisson distribution from missing observations
I am interested in fitting a Poisson/negative binomial distribution to estimate the number of times a phenomenon happens within a period, let's just say 10 years. I can count the events from monthly ...
3
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2answers
95 views
Chart/visual for negative binomial regression
My wife is presenting a study at a conference poster session. She has a correlation of sorts [1] where kids' self-assessment of asthma ("I felt a lot better" or "I felt a little better or not better") ...
0
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0answers
10 views
Unable to load gamlss.mx package [migrated]
I am working on building a generalized linear mixed-effects model with Poisson distributed errors. I worked through it using glmer() in the lme4 package, but realized that my model is very ...
5
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2answers
92 views
What is an Hypergeometric distribution where the last event is success?
I'm trying to find out the name of a distribution that is like negative binomial, only for finite population and without replacement. Or like Hypergeometric distribution where the last event has to ...
4
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1answer
95 views
Overdispersion parameter
I am modelling a zero-truncated process with a count model, and am trying to determine whether the data are overdispersed. The Poisson distribution has a variance equal to its mean,
...
3
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0answers
200 views
Unable to fit negative binomial regression in R (attempting to replicate published results)
Attempting to replicate the results from the recently published article,
Aghion, Philippe, John Van Reenen, and Luigi Zingales. 2013. "Innovation and Institutional Ownership." American Economic ...
2
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1answer
34 views
How to find the distribution of the result of a compound experiment
I'm trying to find the distribution of data collected from a die-roll and coin-toss experiment. The experiment is as follows:
1)Roll a fair die so that you get a number $D \in (1,...,6)$
2)Flip a ...
1
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0answers
68 views
GLM experimental design issues for count data in landscape experiment
I am analyzing bird count data from surveys conducted each week (from Nov-April, when bird foraging most active near breeding cycle) for 6 years in 9 large experimental plots that are split amongst 3 ...
1
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1answer
29 views
How many measurements (10 million molecules each time) are needed to analyze 15 million unique molecues in a 20 million pool?
This is my homework: There are 20 million DNA molecules in a library for high throughput DNA sequencing, each sequencing run can generate 10 million reads (i.e., analyze 10 millions of DNA molecules), ...
0
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0answers
23 views
Is it possible to find out how unlikely a given Beta(alpha,beta) – Beta(alpha’,beta’) is?
X_i -> the distance between two nodes in a graph.
X_i ~ NB(r,p) where p ~ Beta(alpha, beta).
After the posterior hyperparameters are obtained,
Beta(alpha,beta) – Beta(alpha’,beta’) could be found ...
4
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3answers
367 views
Comparing two vectors from negative binomial distribution in R
I'm using R and have two vectors of discrete values. They are not strictly speaking categorical because the values themselves are number of dots counted on the image of a cell (whole vector is all the ...
0
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1answer
158 views
How can I interpret coefficients of categorical predictors in the negative binomial regression model?
I used some categorical variables as predictors to a negative binomial model. The dependent variable is numerical. I used glm.nb in R and the results show relative coefficients of one category ...
1
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1answer
67 views
what kind of regression model I should use for integer and bounded dependent variable?
I am trying to model the rating of a product (that takes integer values between 0 and 10) using some other predictors.
Can I use negative binomial regression? The data is over-dispersed toward higher ...
2
votes
1answer
295 views
How to get the standardized beta coefficients from glm.nb regression in R?
I'm working in R, using glm.nb (of the MASS package) to model count data with a negative binomial regression model. I'd like to get the standardized (beta) coefficients from the model, but am given ...
4
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2answers
304 views
How to compare coefficients of a negative binomial regression for determining relative importance?
I'm working in R, using glm.nb (of the MASS package) to model count data with a negative binomial regression model. I'd like to compare the relative importance of each of my predictor variables ...
0
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0answers
34 views
Binomial distribution conditional probability [duplicate]
Possible Duplicate:
What is the probability of tossing k heads in n trials of a fair coin?
What is the probability of tossing $k$ heads in $n$ trials conditional that in first $t$ attempts ...
2
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0answers
50 views
conditional on the total, what is the distribution of negative binomials
If $x_1, x_2, \ldots, x_n$ are i.i.d. negative binomial, then what is the distribution of $(x_1, x_2, \ldots, x_n)$ given
$x_1 + x_2 + \ldots + x_n = N\quad$?
$N$ is fixed.
If $x_1, x_2, \ldots, ...
1
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0answers
44 views
Model selection using multivariate normal as input and multivariate negative binomial as response?
I'm trying to determine a good model to use to predict multivariate count data given a row of multivariate normal as inputs.
The training set is N*D and the response set is N*P, where N is the ...
0
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0answers
95 views
Using GLM with germination percentages
I have germination data in percentages (with a few zeros) and wish to fit a GLM on them to explore how different seed origins and levels of treatment affect them.
My results are not exactly 'counts', ...
2
votes
0answers
176 views
Negative binomial regression does not converge
I am trying to use a Negative Binomial regression model for some count data in which the dependent count variable takes on the values 0, 1, 2, 3 or 4. I am using SAS and keep running into the ...
1
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0answers
248 views
Calculating predictions and confidence intervals from a negative binomial distribution
I'm trying to use R's glm.nb to calculate predictions and confidence intervals.
When I'm using linear models after training a model, e.g., using:
...
2
votes
0answers
55 views
Manipulating ordinal variables to segment data
Im working with a negative binomial regression where the dependent variable is number of trips by any given mode (i.e. car trips, train trips etc).
I would like to create a variable to segment the ...
3
votes
1answer
601 views
Zero-inflated negative binomial mixed-effects model in R
Is there such a package that provides for zero-inflated negative binomial mixed-effects model estimation in R?
By that I mean:
Zero-inflation where you can specify the binomial model for zero ...
7
votes
4answers
825 views
Poisson is to exponential as Gamma-Poisson is to what?
A Poisson distribution can measure events per unit time, and the parameter is $\lambda$.
The exponential distribution measures the time until next event, with the parameter $\frac{1}{\lambda}$.
One ...
11
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4answers
769 views
Framing the negative binomial distribution for DNA sequencing
The negative binomial distribution has become a popular model for count data (specifically the expected number of sequencing reads within a given region of the genome from a given experiment) in ...
4
votes
1answer
416 views
How is the intercept calculated in a generalized linear model and why is it different from a linear model?
I have a set of count data that I have fitted both a linear model to and a Poisson generalized linear model. The mean of the raw data is 233.375 and the standard deviation 279.983. I have been ...
3
votes
2answers
138 views
Advice on regression modelling
I have a large set of data for 37 different clinical units (all oncology) in their respective 37 hospitals. There are two specific outcome variables that I need to analyse:
First, drug usage for ...
6
votes
2answers
133 views
Experimental design & questions on use of generalized linear models
I have an ecological experiment for which I need to analyze bird count data. Here is the set up:
2 treatments (open/control), 3 regions. Not quite a full 3x2 factorial because in 2 regions there are ...
1
vote
1answer
364 views
What is the reason for differences between nbreg and glm with family(nb) in Stata
I am a novice Stata user (forced here from R to conform to coauthor's quirky option choices). I need access to the Pearson residuals from a negative binomial regression. I currently have the ...
0
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1answer
166 views
Negative binomial — IRR interpretation for predictors
I have a zero-inflated negative binomial model. I have used incidence rate ratios and I'm trying to interpret the coefficients in relation to my predictors. Most of my predictors are continuous ...
4
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1answer
110 views
Modelling both mean and dispersion of count data
I have a model of the following form:
$P(Y \mid X) = \,D(\mu,\sigma^2) ~~\text{where}$
$\mu = f(X) ~~\text{and}~~ \sigma^2=g(X)$
where $y$ is the response vector of count data, $X$ is the predictor ...
4
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2answers
451 views
R equivalent to cluster option when using negative binomial regression
I am trying to replicate a colleague's work and am moving the analysis from Stata to R. The models she employs invoke the "cluster" option within the nbreg function to cluster the standard errors.
...
1
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0answers
191 views
What is the distribution of theta in a negative binomial model (glm.nb with R)?
Good morning every body.
My question concerns the distribution of the $\theta$ parameter in a glm with a negative binomial distribution, such as $V(X)=\mu+\theta\mu^2$.
Indeed, $\theta$ is expected ...
1
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0answers
111 views
negative binomial GLM with time elapsed as a predictor
Suppose you have a response variable, number of transactions a customer makes in the 24 hour period following acquisition, which follows a negative binomial distribution. Let's call the response ...
4
votes
1answer
397 views
Method to estimate the prediction interval for GLM and negative binomial distribution
I used a Monte-Carlo approach to estimate the prediction interval for a new observation from a GLM using a Negative Binomial distribution. I used this method for linear models and got reliable ...
0
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0answers
253 views
Negative binomial regression with $\beta>1$
In analyzing my non-independent count data with a negative binomial regression, I am finding that one of my variables (a proportion variable) consistently is showing a beta weight > 1. As a matter of ...
0
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1answer
48 views
Best distribution to model durations
I am struggling to find an appropriate distribution to model the total number of vacation days taken in a year.
I originally thought of going with a standard count distribution like negative binomial ...
2
votes
1answer
838 views
How does glm.nb work?
I have been working with glm.nb from MASS package for quite a while now. However, there are somethings I seem to not quite able ...
3
votes
1answer
1k views
Model validation after fitting a negative binomial GLM in R
Ok, I have searched and searched and just have no clue where to start. First, what I would like to do is produce a QQ-plot (or even a readable residual plot) to look at the fit of my model. I guess ...
0
votes
0answers
18 views
unstandardized coefficients where mean-centered X variables have +/- values from 0 [duplicate]
Possible Duplicate:
How to interpret regression coefficients for a variable with takes positive and negative values?
I'm performing negative binomial regression and one of my centered-mean ...
1
vote
1answer
162 views
Multiple testing correction
I use glm.nb from MASS package over many observations. I then extract the p-values for the interaction term. Due to multiple ...
0
votes
1answer
99 views
Can a factor be changed to binomial levels to achieve model validation and extract insignificant variables?
I have just run a NBGLM and want to know something. If I am aiming to drop the least significant explanatory variables until all explanatory variables are significantly correlated wih the response ...
3
votes
1answer
340 views
Use of a negative binomial model for fitting alternative splicing event
I am working on RNA-Seq data (on alternative splicing). Let's say I am looking at a particular type of alternative splicing event - exon skipping. For each intron (or junction), I look if it is ...
5
votes
1answer
466 views
Quasibinomial vs negative binomial and hurdles
I have some over-dispersed data and am trying to decide which model would best suit the data. The data are usually counts of symptoms or number of correct items on some cognitive tasks. As an example:
...
0
votes
0answers
177 views
A strange coefficient in a negative binomial model
I already asked a first question here fortunately or unfortunately the answers support my understanding of the general problem. Too bad this doesn't solve the problem.
So, here is the thing:
I am ...
5
votes
2answers
3k views
Interpretation of incidence-rate ratios
So, I want to fit a random effects negativ-binomial model. For such a model STATA can produce exponentiated coefficients. According to the help file such coefficients can be interpreted as ...
5
votes
1answer
211 views
Selecting regression model for a non-negative integer response
I have a series of non-negative integers $y=(y_1,y_2,..., y_n)$ and a design matrix $y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_3 x_1 x_2$, where $x_0$ and $x_1$ are $0$ or $1$, $x_1x_2$ is the ...
2
votes
2answers
664 views
GLM model validation
When performing model validation and we are dropping the least significant explanatory variables until we find the optimum model where all remaining variables are significant, how does one go about ...
7
votes
1answer
752 views
Distribution that describes the difference between negative binomial distributed variables?
A Skellam Distribution describes the difference between two variables that have Poisson distributions. Is there a similar distribution that describes the difference between variables that follow ...
