Variables that are counts (non-negative integers) often have an excess of zeroes. Zero-inflated regression models (e.g. zero inflated Poisson, zero inflated negative binomial) are designed to deal with this. Less commonly, continuous data can have this issue, and there is zero-inflated normal ...

learn more… | top users | synonyms

0
votes
0answers
14 views

Dealing with sampling zeros

I would like to perform a $\chi^2$-test, but due to low sample size in some cases, I have sampling zeros. One example of such a case: ...
0
votes
1answer
15 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 ...
0
votes
1answer
27 views

Calculating OR and IRR for a zero-inflated negative binomial model from estimates

I used the PSCL package to run a zero-inflated negative binomial model on some count data I have. This package gives the following output: for the zero part of the model: ...
1
vote
0answers
35 views

What is effect on PCA of having too many zeros in the data?

I want to use Principal Components Analysis to derive dietary patterns. However, my data have many zeros (no intakes) for many observations. I'm unable to find relevant literature to know how biased ...
0
votes
0answers
15 views

Gamma/ inverse.gaussian glmer with zeroes that matter

I'm trying to analyze continuous non-negative data with real zeroes that matter. The data are either gamma or inverse.gaussian distributed (from gamlss package) and ...
0
votes
0answers
13 views

How to model Zero-inflated count vector with random parameters?

I am trying to estimate a parameter vector $(\theta_1, \ldots, \theta_n)$ from a vector of observations $(y_1, \ldots, y_n)$ where $Y_i \sim Poi(\theta_i)$. The data is zero-inflated in the sense that ...
0
votes
0answers
15 views

Right censored data, abundant in zeros for regression analysis

I am looking at conditioning to stimuli and there in the time taken to perform a certain task. The IV for this data is Conditioning periods ranging from 1-34 periods and the DV is the time taken for ...
1
vote
1answer
25 views

Modelling zero-inflated continuous data

I'm currently trying to apply a linear model (family = gaussian) to an indicator of biodiversity that cannot take values lower than zero, is zero-inflated and is continuous. Values range from 0 to a ...
0
votes
0answers
10 views

SEM with zero-inflated outcome

I'm working on a project and my advisor wants me to do SEM in MPLUS because we can do latent modeling, but I have no clue where to start. I've only ever done regressions but I said I'd be up for ...
0
votes
0answers
37 views

Set G in prior for MCMCglmm in R

I am new to the MCMCglmm package in R, and rather new to glm models in general. I have a dataset of species traits and whether or not they have been introduced outside of their native range. I would ...
1
vote
1answer
26 views

Multicollinearity in Zero Inflated Negative Binomial Regression

I am trying to model counts govt, based on the counts lp,const,...
0
votes
0answers
29 views

Modeling with zero-inflation models for prediction

Below is the summary of my dataset: ...
1
vote
1answer
31 views

Prediction from Zero Inflated Models?

I have fitted a two stage generalized additive model to zero inflated data ("hurdle model"). I am modeling fishery spatial data (catch rates) as a function of different environmental variables ...
2
votes
1answer
32 views

Regression problem in data with many zeroes

This is my first question here, so please bear with me. I am working on a regression model to estimate the long term value of users of a certain website. For example, I would like to estimate the ...
0
votes
0answers
8 views

Regression coefficients versus marginal probabilities

I ran a negative binomial regression model and found significant coefficients for several key variables. I also ran the model as a zero inflated negative binomial model, and didn't find significance. ...
0
votes
0answers
46 views

How to model these data? (zero inflated, positive skew,

- The variable depicted in the histogram is a sum of 11 items. Each item asks about the frequency of occurrence of a particular type of event (a negative consequence of drinking) on the following ...
1
vote
0answers
22 views

Canonical Link Function in Zero-Inflated Poisson Model

For a zero-inflated Poisson model, I understand that the Poisson Model uses the canonical link of log(mu). For the logistic portion of the model, I usually see the equation written as logit(phi), and ...
0
votes
0answers
38 views

Zero-inflated Poisson regression Vuong test: Raw, AIC- or BIC-corrected results

I'm analyzing count data for a set of ten species and found that for the five species with highest detection rate, the zero-inflated poisson (ZIP) regression fits the data significantly better than ...
2
votes
1answer
50 views

Zero-inflated Poisson regression: how can I calculate contrasts for BACI (Before-after-control-impact) experiment?

I am analyzing count data with a lot of zeros and found that although the data do not fit a poisson glm, they fit the zero-inflated poisson (ZIP) regression significantly better than the standard ...
0
votes
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 ...
0
votes
0answers
13 views

estimating probably of false zeros using ziP GAM

I pulled the code below from Zuur et al. 2009 (was used to estimate probability of false zeros with a zeroinf() model) and applied it to a ...
0
votes
0answers
19 views

Statistical Procedures in Heavy Zero Density Data

How do procedures such as Principal Component Analysis, Logistic Regression, Cross Validation perform under Zero under Zero Heavy Data? Are they sub-optimal or simply inadequate?
0
votes
1answer
31 views

Dual Quasi-Newton Optimization on Zero Heavy Data (in SAS)

I am using PROC FMM in SAS, in attempt to use hurdle models on a data set with many zeros. There is one response variable and it's continuous, there are ~90 predictors (continuous - but contain many ...
2
votes
2answers
84 views

Modelling Data with Many Zeros - Principal Component Analysis vs Zero Inflated Models

I have a data set (many continuous predictors, single response variable that is also continuous) with many zeros. I first used PCA and found the results to be very helpful. I further thought that the ...
0
votes
1answer
38 views

Zero-inflated models how to get predicted values = 0 stata

I'm fairly new at Stata and this is the main reason of my question. I did a longitudinal zero-inflated poisson model: ...
0
votes
0answers
13 views

zero inflated negative binomial versus earth to describe data

Hi is earth function in the earth package appropriate for zero inflated negative binomial data? Is the apparent exponential curve an artifact of erroneous application by me? Opinions very welcome. I ...
0
votes
0answers
33 views

How to manually calculate fitted values in zero count models?

I'm looking to see if I can manually calculate the fitted values for a Hurdle model (from the pscl package) to ensure I understand what's going on, but so far I ...
1
vote
0answers
44 views

Zero Inflated Poisson Regression in R

I have a zero inflated Poisson regression model I want to run R, as my data are over-dispersed and I have many zeros, but I am not sure how to set up the model in R and interpret the output. First, is ...
0
votes
1answer
95 views

Non-parametric test of difference for zero-inflated data

I have zero-inflated (~90% zeros) data which is distributed like the left-hand figure above (the right-hand figure shows how when log-transformed, the non-zero component of the distribution is ...
0
votes
1answer
56 views

How do you deal with overdispersion in a zero-inflated negative binomial regression AND when you expect data to have zeros?

Background: I am analyzing the effect of multiple variables (lineage, ancestral plant species, plant species reared from, larval density, body mass) on different traits: ovigeny index (initial egg ...
0
votes
0answers
48 views

Choosing between Zero Inflated Negative Binomial model versus Logistic Regression

Context: this is in the field of genome wide association studies. The norm in the field is logistic regression, but we have high quality radiographic data that gives us counts of damaged joints, so I ...
1
vote
1answer
49 views

linear mixed model on continuous, zero-trunctated data

I have a dataset with one continuous response variable (time), a 'treatment' explanatory variable and 5 other fixed factors: ...
1
vote
0answers
18 views

How to use ln-transformation with loads of zeroes?

I came across a method of ln-transforming exogenous variables in non-negative data sets with loads of zeroes in a lecture. With the method proposed, one simply ln-transforms the variable in question ...
2
votes
0answers
152 views

Gamma hurdle model for continuous response

I am modelling invertebrate.biomass ~ habitat.type * calendar.day + habitat.type * calendar.day ^ 2, with a random intercept of transect.id (50 transects were repeated 5 times) My response is ...
1
vote
2answers
162 views

Penalized regression with zero-inflated models

I'm currently building zero-inflated Poisson & negative binomial predictive models using the zeroinfl() function from the pscl package in R. Incorporating penalized regressions into my model to ...
0
votes
1answer
61 views

A model for technical measurement data with many zeros - pros and cons of Tweedie

I analyze technical measurement data with the aim of developing a forecasting model. The data is given as a non-negative time series (data per hour). The data looks quite wilde and contains many ...
0
votes
1answer
75 views

Dealing with zero-inflation if the data are not count data type

In the literature I found that for the count data with a lot of zeros so-called zero-inflated distributions (models) and so-called hurdle-at-zero distributions (models) could be used. The differences ...
0
votes
0answers
107 views

Zero-and-one inflated beta regression vs. binomial GLMM?

I appreciate some help with deciding whether I should (and how to) construct a zero-and-one-inflated beta regression model. I want to use R to test the hypothesis that there is a ...
1
vote
0answers
41 views

What is expected count formula for zero-inflated negative binomial regression?

My IT department wants me to translate my zero-inflated negative binomial regression model into a formula for calculating expected count which they can hard code into SQL. I'm running the model in ...
0
votes
0answers
40 views

Help with highly skewed data

I have a response variable which is highly skewed and has a high percentage of zeros. I am looking for some guidance around what modeling technique to use and the process to follow. As an ...
1
vote
0answers
61 views

What statistical test should I use for variable which has all values identical and zero?

I'm a neuroscientist and I count protein aggregates in the brain. I use tissue from MND patients with and without a certain mutation, and from healthy controls (3 groups in total). When I use healthy ...
0
votes
0answers
84 views

Marketing Data with many zeros

I am working on a marketing data which is a time series data with marketing spend done through different channels and revenue generated. The data looks like this : My data contains too many zeros ...
4
votes
0answers
192 views

How to model Zero-one Inflated Proportion Data?

I have a problem with my dependent variable, which is a proportion including ones and zeros. I am analyzing the use of a fungicide in apple farming. I have a sample of a survey of 1300 farmers and ...
1
vote
1answer
99 views

glm.nb fails to converge when adding one zero

I have a problem where glm.nb (R version 3.1.0, MASS version 7.3.33) converges on some data, but adding only one 0 it does not converge any more. This is the data ...
0
votes
1answer
74 views

Transform response for hurdle model

I am using a hurdle model (dist=negbin, link = logit) for a dataset with multiple explanatory variables, excessive zeros and overdispersion by both, zeros and count data. The residual plots (pearson ...
1
vote
0answers
21 views

How to model the following dependent variable? [closed]

If I got a dataset which the (count data) dependent variable has the following distribution, how should I model it? I am aware of the zero inflated model and the negative binomial model, but are ...
3
votes
0answers
67 views

Appropriate test (in R) for proportion data that aren't normally distributed, aren't based on counts, and include 0's and 1's?

I'm studying differences in tree health among 5 species of trees across 3 different green infrastructure types. Here are the first few lines of data: ...
2
votes
1answer
61 views

Zero-inflated gamma - how to write down the cdf?

My goal is building a predictive model to give probabilistic forecasts. My response variable has lots of zeros but otherwise looks close to a gamma. I fit the whole dataset using some classification ...
0
votes
1answer
40 views

What is the relationship between zero-inflated binomial and Bin(n,p) distribution?

Hi just could anyone explain the relationship between zero-inflated binomial and $\text{Bin}(n,p)$ distribution? I think zero-inflated binomial should be a kind of mixture and $\text{Bin}(n,p)$ and ...
1
vote
1answer
117 views

General estimating equation with zero-inflated continuous data in R

I'm running a General Estimating Equation using the geepack package in R. I'm wondering what distribution family would be appropriate for my data, which are zero-inflated and continuous. I would have ...