Questions tagged [zero-inflation]
Excessive 0's in a variable compared to a specified reference distribution. Regression approaches include zero-inflated models and hurdle (2-part) models. For count data, zero-inflated and hurdle models based on Poisson or negative binomial distributions are common (ZIP/ZINB and HP/HNB).
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GLMM optimised with CG gives empty warning, diagnose() finds no problem
I am running the following code for a ZINB GLMM, using the glmmTMB package in R:
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How to construct homogeneous subsets table for nonparametric tests?
Does post-hoc for friedman tests or nonparametric testshave like a homogeneous subset table from SPSS? Mean doesn't represent it well so I tried using median but my data was zero-inflated so the most ...
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Descriptive Statistics for Zero-Inflated Dataset
Just as titled, I'm curious as to what should you choose to represent a measure of central tendency for a zero-inflated vector or such. My background says go "mean" if normally distributed, &...
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Lognormal including 0
I'm trying to model a random variable $X_i$ related to updates in prices. The updates in prices are always non-negative, and my random variable is the update coefficient. For example, if product $a$ ...
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Is it possible to correct a zero inflated explanatory variables in a GAM?
I am trying to fit a GAM to model a presence/absence of marine mammals in function of temporal and environmental variables using MGCV (hence using binomial distribution). My 'precipitation' ...
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Why would a model indicate overdispersion without random effects but underdispersion with random effects? (and how to handle)
Overview: In my model building process, I fit both GLMs and GLMMs. I noticed that the GLMs suggested overdispersion in the data, while the GLMMs suggested underdispersion. How can I make sense of this,...
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Relative importance in hurdle model: which metric to use?
I want to calculate the relative importance of predictors of a hurdle model, my first choice is dominance analysis. For that I would need a suitable metric of model quality. My first thought is to use ...
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Zero- and one-inflated beta GAMM (Generalized additive mixed model) in mgcv
I have vegetation cover (%) data [0,1] that includes 0's and 1's that I'd like to model with a beta GAMM, but don't understand the method for doing so. I've read that if the data includes 0's and 1's ...
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is there a way to to account for time-varying covariates in a negative binomial hurdle model?
i am investigating smoking exposure (0,1) and caries outcome (count data, 0-28). I am using a negative binomial hurdle regression model (considering excess zeros in outcome) for my analysis
Model 1:
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How do zero values affect the correlation between two twitter activity time series?
I have a time series of Twitter activities (lets call it time series 0) from which I extract two separate time series based on different sets of users (i.e. time series 1 contains the activity of a ...
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Testing zero-inflated negative binomial models using Anova function
I used the glmmTMB package to fit zero-inflated GLMMs with negative binomial error distributions (example below):
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How to deal with overdispersion with glmmTMB for generalized linear models
I'll try to make it as brief as possible.
I'm trying to fit a glm to echolocation clicks count data using the glmmTMB function. I started with a Poisson glm and ...
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Adding predictor variables to hurdle model
I am trying to build GAMs to understand the detectability of two cetacean species using acoustic and visual data. For now, I am running three separate models (All detections (encounters), Acoustic ...
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Dummy variables Zero inflated Ordered probit mods
I am relatively new to zero-inflated ordered probit models, and I've observed a pattern in the analysis of discrete data. In the research papers I've come across, many discrete variables are grouped ...
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How do I associate or assign a large amount of continuous variables with zero-heavy distributions to different groups?
I have a dataset with about 70 continuous numeric variables. I have about 80 samples which divide more or less equally into two groups. I want to figure out which of these variables is most strongly ...
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Difference between running a zero-truncated model on zero-truncated data and running a GLM on zero-truncated data
I apologize if this a remedial question but I am trying to determine how best to proceed with my analysis and I would appreciate if someone could explain the differences in assumptions between a zero-...
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How to make predictions with parameters obtained from a zero-inflated Poisson model with Bayesian approach?
I have a dataset with one response variable and one explanatory variable. Here is my zero-inflated Poisson model with a Bayesian approach.
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Help interpreting zeroinfl results from emmeans
I am working on the example Senecio data from Blasco‐Moreno et al. (2019) using the pscl package in R. I would like to conduct pairwise comparisons of mean rates (Damaged/Total_heads) and don't ...
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Zero-inflated Poisson - Implementing INLA with two likelihoods
I am trying to implement a zero inflated model in INLA.
I know a basic zero inflated Poisson can be implemented with "zeroinflatedpoisson1" as the family ...
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Model non-negative zero-inflated with continuous data?
I have a few observations (58) because my same is really small since we're studying the performance of some incredibly rare individuals. Anyway, we're measuring the time durations for some behaviours ...
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Zero inflated Ordered Probit/Logit Model
I have data with some ordinal component of Injury severity, measured as property damage, Fatal or Killed.
Is there an R package for the Zero-inflated ordered Probit / Logit models?
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Interpretation of glmmTMB output for zero-inflated negative binomial regression
I fitted, using glmmTMB R package, a zero-inflated negative binomial GLMM, with offset and a random factor, to investigate which variables could explain animal ...
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Percentage data with many 100 values
I am looking at the accuracy percentage that has a distribution with several $100%$ values. These are true $100%$ values since most people have high accuracy in some conditions. Here is the ...
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Statistical tests on zero-inflated samples
I have a lot of zeros in my samples; I found the following algorithm to deal with zero-inflated data before testing them:
find the proportion of zeros in each subset.
assume that in the subset with ...
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Zero inflated Model Olympics medal count
I am trying to build a model for predicting Olympics Medal counts.
My data looks like this;
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How do I deal with many zero values in terms of correlation?
I am currently working on my master thesis. I have determined a simulation model for tax returns and to verify this I would like to use true values.
I would like to determine the correlation between ...
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Elasticity estimates for zero-truncated negative binomial part in the hurdle model
I estimated a hurdle negative binomial regression model with zero-truncated negative binomial model as the count component in R using the pscl package. I wish to present elasticities for the count ...
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Comparing Defects per Volume
Let's set up a toy problem. Say I make a vat of soup every week. Different kinds of soup, different volume each week (e.g. 50 gallons, 46 gallons, 10 gallons, etc.) and sometimes there are flies in ...
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Error when using R zeroinfl on negative binomial count data model
I want to model number of plants in 12 different areas.
Each area is divided into smaller plots of equal size for all areas. The number of plots between areas vary (31 for the smallest area, 500 for ...
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ARIMAX model for Google trends: trouble with lots of zeros
I want to apply ARIMAX model on Google trends. I used python package to get daily data. However, this data contains a lot of zeros, so if I do first difference of logs, I see a lot of (inf) in python.
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How to deal with zero inflated panel data?
For a research project, I created a panel dataset that counts violent events in subnational administrative units from 2000 to 2015, thus my unit of analysis is district-year. Out of 20.000 district-...
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Is Repurchase period in days a count variable?
So I am doing a project where the response variable is the number of days in which a product is rebought. Explanatory variables are the weight of the product, serving size etc.
So basically I am ...
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Options for repeated measures ZINB
I'm trying to figure out the best way to analyze a dataset on 48 youth who were part of am 8-week summer program. These youth were given time-outs and then their behaviors were recorded during time-...
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Zero inflation formulas do not improve zero inflated model (GLMM) [closed]
I'm a student trying to adjust a count data GLMM to zero inflation using function glmmTMB.
Model family is nbinom1 family (better AICc compared to nbinom2 and poisson).
My data contains about 20 % ...
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Zero-inflated poisson model producing NaN results [closed]
I have been trying to run a zero-inflated poisson model as follows:
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Poisson GLM with rare outcome (very negative coefficient)
Suppose I have the model
\begin{align*}
Y_i \sim \text{Poisson}(e^{\beta_0 + \beta_1t_i + \beta_2X_i + \beta_3 t'_i})
\end{align*}
where $\boldsymbol{\beta} = (-1, 0, -10, 0)$ for $t_i = i$ for $i = 1,...
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RJAGS - Zero Inflated Negative Binomial RJAGS
I am trying to fit a RJAGS zero-inflated negative binomial model. The data I am using has 451 observation and only 12 of them have values different to 0, which means that 97% of my observations are 0. ...
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How to think about and select variables for the zero-inflated part of the ZINB
The question of modeling the zero-inflated part of a negative binomial mixed effects model is a thorn in my side. I've read a lot of articles and blogs and it seems to be an issue that is largely ...
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Beta Binomial Distirbution - Updating beta with 0 occurence [closed]
I am dealing with different problem where a count data has to be modelled either with binomial distribution or hypergeometric. I have done a extensive literature read and it seems that occurence equal ...
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Hurdle model for non-count continuous data with both positive and negative values
I am aiming to estimate a hurdle model (where being non-zero is the hurdle) in the vein of Cragg. My data is both positive and negative where the data reflects the difference between a value reported ...
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Can NB model cope with excess # zeros
I have a counts response variable with 46% of the observations being zeros, and am validating model fits to different distributions with GAMMs. I have plotted the observed number of zeros in the ...
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Statsmodels ZeroInflatedPoisson - Unable To Converge
I've been asked to fit a ZeroInflatedPoisson model on a dataset to predict Y (count data) for an assignment.
First, I did this manually:
Create a binary variable (Y_IND) based on Y where Y_IND = 0 if ...
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Zero inflated model with prediction
I am analyzing host seeking behavior (called questing) of ticks from two populations (Lab and field collected). I have ~20 percent zeros in my dataset. I had 20 ticks per enclosure (where 0 is no ...
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Explanatory model for zero-one inflated bimodal data with random effect and binary indepentent variable
I'm trying to evaluate the influence of a single binary explanatory variable on a 0-1 scale response, with one grouping factor. The response variable is generally 0-1 inflated. The simplest solution ...
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Justification for using zero-inflated model in GLMM
I am using GLMMs in R to examine the influence of various continuous predictor variables (x) on several biological counts variables (y). My response variables (n=5) each have a high number of zeros (...
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Issues with my Dependent Variable consisting of ~95% zeros
I'm currently struggling with my hurdle-regression analysis. My sample consists of randomly selected people in my hometown. The sample size is around 600 participants. My DV (count of car thefts) has ...
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How can I calculate growth per year when some of the size values equal zero?
I am trying to retrospectively evaluate growth of tumors over the years. I am looking at the tumor's initial presenting characteristics:size, location etc., and then following to see which of the ...
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Statistical testing: do count data come from the same distribution?
The data I am dealing with are groups of counts, $n_i, i=1..K$. More than a half of these counts are zeros. The null hypothesis is that all the counts come from the same distribution, e.g., Poisson ...
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Method comparison: removing double-zeros before orthogonal regression, deming or passing-bablock?
I am comparing two pieces of equipment / methods which are supposed to measure the same information over time.
I looked into the literature and decided Passing-Bablock seemed like the right approach (...
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Zero-inflated negative binomial results interpretation & using * instead of + for categorical variables
I use RStudio. I have been working with zero-inflated models, particularly the zero-inflated negative binomial. COUNT is the numerical value that refers to the number of a fish species. FIBISTRATA is ...
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