Count data are non-negative integers representing whole amounts. When such data are the dependent variable in a regression, Poisson or negative binomial regression may be appropriate methods. One common problem is "zero-inflation" (where the proportion of zero values is greater than predicted by a ...

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35 views

Using Poisson GLM for visits to a historical monument - Am I using the right method?

Dependent variable - number of visitors to a historical monument by day Independent variables - Daily average temperature, relative humidity, number of tourists visiting the state by day, etc. My ...
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9 views

Iterative Maximization issue in Truncated Negative Binomial Regression in Stata

I was running truncated negative binomial regression in Stata and got a problem. During the iteration process, my results show " backed up" at the end of final iteration which means Stata could not ...
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10 views

Truncated Negative Binomial Regression (Stata): Missing/Blank Significance value

I was running truncated negative binomial regression (tnbreg) in STATA and got the answer but when I added [pweight = weighting variable ] to weight dependent variable to address endogenous ...
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4 views

Compare related time-series count data before and after intervention

For a university library we did an intervention to create more free seating spots for students by introducing a way for students to temporarily give up their spot while away. They did this with an ...
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1answer
14 views

Rules for Percentage of zeros in a zero inflated model

What percentage of zeros in the data should make us consider trying the sequence of models: Poisson -> Negative Binomial -> Zinf-Poisson -> Zinf-Negative Binomial, etc? I have two datasets with about ...
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12 views

Use of mixed effects model for count, continuous and binary variables

I have data in the following structure: Nested: "site" (n=6) > "year" (n=6) Response: "marshland_area" (continuous) Explanatory: "sea_level" (continuous); "invasive_species" (binary); ...
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1answer
37 views

Test if number of fish differ by location

I have a dataset which consists of location ID's (1 to 29 different locations) and each location has a couple of repeated measures (max n = 780, min n = 50). Each measurement consists of a number of ...
0
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1answer
18 views

Confidence level of a sample of count data

I have a sample of count data (N = 226) representing a parameter of a population. The sample contains many zero values and a few non-zero values. How can I best estimate the level of confidence that ...
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2answers
50 views

After how many events can you say the failure rate of one piece of equipment is greater than another?

If there exist two identical pieces of equipment, in this case two pumps that are the exact same (in theory) pumping the same fluid in the same location at the same rate (everything is the same), how ...
3
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1answer
40 views

Time-dependent Poisson regression

I have a time series that count the number of "type 1" events in a city, for each day. The serie contains a lot of zeros because type 1 events are rare (about 80% of counts are zeros). I'm using a ...
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19 views

How to calculate standard error for WEIGHTED count data aka proportions

First let me explain my data. I have 30 residential subdivisions. Within each subdivision, I randomly generated n=50 points over an aerial photograph. For each subdivision, I counted how many points ...
3
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1answer
41 views

Confidence interval for population mean when sample is a series of counts?

I have count data for each of a sample of individuals (it's the number of times each independent individual performed a certain behaviour during a standardised observation of that individual). How can ...
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2answers
18 views

General linear model for counts which are “correlated”

The typical general linear model (GLM) for count data uses the Poisson link function. The counts there are assumed to be "independent". Now suppose the counts are not "independent" in a sense ...
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2answers
67 views

Can I ignore under-dispersion in my count data?

I have under-dispersed count data. I do not want to transform them, and using a negative binomial error distribution (via glmer.nb) does not help. My results are the same regardless of the ...
10
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1answer
198 views

Diagnostics for generalized linear (mixed) models (specifically residuals)

I am currently struggling with finding the right model for difficult count data (dependent variable). I have tried various different models (mixed effects models are necessary for my kind of data) ...
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1answer
26 views

Multicollinearity in Zero Inflated Negative Binomial Regression

I am trying to model counts govt, based on the counts lp,const,...
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1answer
19 views

modeling count data with underdispersion

I modeled the count data with poisson regression and the pearson chi square divided by the degrees of freedom was 0.25 suggesting under-dispersion , what can I do , is it possible to deal with this ...
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25 views

Exact test for m x n contingency table conditional (i.e. fixed by design) on one margin

I have a $m \times n$ contingency table (with $m, n > 2)$ . The experiment yields ~15% cells with expected frequencies lower 5 and also zero counts in the empirical data. The prerequisite for the ...
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2answers
29 views

count data that does not follow poisson distribution

Im analysing count data (number of clinic visits) when I test for poisson distribution using 1 sample KS test in spss the sig is <0.05( meaning it doesnt follow poisson distribution ) it does not ...
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1answer
35 views

count data that does not fit anything

I am trying to build a counts model but my response does not seem to fit anything. If I pull the histogram looks poisson-ish but when I run goodfit() in R, it does not fit poisson or negative ...
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1answer
24 views

In count data models with dummies, what exactly means “on average”?

There are some questions + answers out here that explain how to interpret coefficients from count data regressions (e.g. negative binomial), both as incidence rate ratios or marginal effects. Bottom ...
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27 views

Relationship between dispersion statistic and variance in count data models?

I am struggling to get my head around the concept of data dispersion, particularly relating to count models. Take for example, the Poisson regression model, I will often read that if the variance of ...
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1answer
46 views

Standardizing or normalizing count data

I have a dataset where I counted the number of a species in different environments and grouped it into different categories ranging from 0 to 5. 0= no occurrence; 5= very high occurrence. All ...
0
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1answer
34 views

Relationship between two zero-inflated counts varying in space

I have two variables where each observation represents counts at some point in a discrete 1D space (along an RNA sequence). The space is finite, and the counts are highly zero-inflated compared to a ...
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46 views

How to estimate probabilty for count data where each count occurs only one time?

I would like to estimate probability of labor mobility from one industry to another one. As an outcome variable I took number of hiring and explanatory variables are separations, differences in wages ...
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1answer
86 views

Best regression model for time series data

Suppose I have a Count time series data for the number of tasks that a server gets during a unit of time. Collecting data over a few months, I will get a dataset which will have 2 parameters. First ...
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1answer
69 views

Fitting continuous data with zeros to a discrete distribution

I have data on the abundance of a particular organism across a sampling area. However, instead of counts, I have the estimated biomass of the organism at at each sampling location (that is, the ...
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15 views

Mixing time to first event model with a count model

I want to build a model of sales productivity where productivity ramp-up involves an unknown period of zero productivity followed by productivity that follows a standard count distribution such as the ...
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16 views

Picking Optimal Sampling Frequency in a Longitudinal Experiment

I am trying to design an experiment to determine the scale at which an underlying process is occurring in a multivariate time-series of counts (alternatively it can be viewed as trying to find the ...
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31 views

Appropriate random effects GLMM analysis for mean count data? R

I'm trying to find the right way of using a mixed effects approach on some mean count data (animal visits per day to a feeder) in R. I have two interacting fixed effects (both factors) and 2 random ...
6
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1answer
747 views

Poisson vs Quasi-Poisson

In modeling claim count data in an insurance environment, I began with Poisson but then noticed overdispersion. A Quasi-Poisson better modeled the greater mean-variance relationship than the basic ...
9
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2answers
143 views

Scale variable as count data - correct or not?

In this paper (freely available via PubMed central), the authors use negative binomial regression to model the score on a 10-item screening instrument scored 0-40. This procedure assumes count data, ...
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0answers
14 views

Selecting an appropriate link function for zero inflated negative binomial regression

I have count data distributed according to zero-inflated negative binomial RV. I have been able to find good sources for a lot of model diagnostic steps, but there are a few things that are eluding ...
0
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1answer
34 views

count time series with multiple seasonality

I am analyzing a time serie (hospital admittance) registered by date - time at hourly frequency, collected all days during 2010 - 2014 (5 years). The time serie exihibits seasonality at multiple level ...
3
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1answer
68 views

Appropriate application of Poisson regression?

I am looking to predict the number of shots that are going to be made by soccer players in their next game. Currently, I use multiple linear regression, where I regress my entire data set (...
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1answer
65 views

Intraclass correlation with count data

I want to calculate the ICC between 3 different measurements where the dependent variable is a count. As far as I understood, if the data were normally distributed, I would use a repeated measures ...
3
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1answer
130 views

Clustering: Do I have to transform all variables derived from a single categorical variable in the same way?

Basic problem Here is my basic problem: I am trying to cluster a dataset containing some very skewed variables. The variables contain many zeros and are therefore not very informative for my ...
0
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1answer
38 views

Ecological modelling: multivariate abundance time-series data

I am working with a dataset that consists of abundance counts of 6 microbial taxa in a lake measured weekly for 20 weeks. I also have environmental data (temperature, nutrient concentrations, ...
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21 views

SEM model using counts in lavaan

I am trying to use SEM for an analysis, but the measured variable to which everything else in my data-set is correlated is an integer. I am not sure if this is the culprit, but I am receiving issues ...
0
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0answers
18 views

Nonparameric mixed (split-plot) anova in SPSS?

All, I have a 4-within x 2-between split-plot (i.e., mixed ANOVA) design with data that are not normally distributed. The DV resembles count data, with mostly zeros and decreasing numbers of 1s, 2s, ...
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29 views

Odd looking residual plot - not sure what transform to use if any

I am concerned about the residual plot shown. The (count) data are over-dispersed, with about 40% 0s, median is 2, maximum is 300 or so. I am not sure what how to proceed with this - it is not ...
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73 views

Should I ignore negative prediction values?

I have the following time series of count data: ...
0
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1answer
34 views

Logistic Growth models for Count Data

I have a dataset of monthly ridership figures by transit route from 2007 to 2015. I am analyzing this data in R. When I go to predict on a new dataset with step increases in trips (ie 1,2,3,etc.) ...
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34 views

How to use multilevel analysis (MLM) in SPSS when I have 1 DV (frequency of absenteeism) and multiple IVs (more then ten) over three levels?

My aim is to analyze data in SPSS from an employee survey (approx 2000 subjects) and link this data to absenteeism. I think I should use multilevel analysis, but I am not experienced with MLM. DV = ...
4
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1answer
307 views

Non-normally distributed data - Box-Cox transformation?

I have data that is not normally distributed. The problem seems to be that there are too many of one value relative to other values. What I have tried to make data normal: I have tried a log ...
2
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1answer
91 views

Creating a probability distribution that is truncated skewed

I have a dataset I want to use to generate a probability distribution. The distribution is skewed and can only include positive integers. I've tried normal (both skewed and truncated, although I ...
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33 views

comparing multiple proportions over time

I have a dataset of 22 fish species in a fish market, sampled monthly, from 2007 to 2011. I want to see if there is a statistically significant change in their relative proportions over time. I first ...
1
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1answer
95 views

PCA on count-based data

I'm looking to do a PCA analysis on count based data itself rather than averages. I'm hoping this will help for variable observation depths; for example, 3/4 reads is not really equivalent to 15/20. ...
0
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0answers
25 views

Comparing hurdle models to negative binomial models

I'm trying to compare the AIC or log-likelihood of a negative binomial GLM to a hurdle type approach, consisting of a binomial GLM for the presence/absence of a count and the counts modelled with a ...
2
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0answers
60 views

Modelling overdispersed counts - past negative binomial

I'm modelling overdispersed counts. I began using a GLM with Poisson error structure, then moved to quasi-Poisson, and then finally negative binomial. The residuals versus fitted values plot is still ...