similar to: How to calculate percentage variation in a zero-inflated negative binomial regression model

Dear All, I am trying to fit some data both as a negative binomial and a zero inflated binomial. For the first case, I have no particular problems, see the small snippet below library(MASS) #a basic R library set.seed(123) #to have reproducible results x4 <- rnegbin(500, mu = 5, theta = 4) #Now fit and check that we get the right parameters fd <- fitdistr(x4, "Negative

Hi, I want using zero-inflated negative binomial regression model to classify data(a vector of data), that is I want know each observed value is more likely belong to the "zero" or "count" distribution(better with relative probability). My data is some like: count site samp 12909 1 1 602 1 2 50 1 3 1218 1 4 91291 1 5

Hi, I am a little confused at the output from predict() for a zeroinfl object. Here's my confusion: ## From zeroinfl package fm_zinb2 <- zeroinfl(art ~ . | ., data = bioChemists, dist = "negbin") ## The raw zero-inflated overdispersed data > table(bioChemists$art) 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1

Hi all, I am running the following model: > glm89.nb <- glm.nb(AvGUD ~ Year*Trt*Micro) where Year has 3 levels, Trt has 2 levels and Micro has 3 levels. However when I run it has a zero inflated negative binomial (as I have lots of zeros) I get the below error message: > Zinb <- zeroinfl(AvGUD ~ Year*Trt*Micro |1, data = AvGUD89, dist = "negbin") Error in optim(fn =

Hi! I'm running a zero-inflated negative binomial regression on a large (n=54822) set of confidential data. I'm using the code: ZerNegBinRegress<-zeroinfl(Paper~.|., data=OvsP, dist="negbin", EM=TRUE) And keep getting the error: Warning message: glm.fit: fitted probabilities numerically 0 or 1 occurred I've done enough reading about this error to realize that I have

Hi, I am new to R and has been depending mostly on the online tutotials to learn R. I have to deal with zero inflated negative binomial distribution. I am however unable to understand the following example from this link http://www.ats.ucla.edu/stat/r/dae/zinbreg.htm The result gives two blocks. *library(pscl) zinb<-zeroinfl(count ~ child + camper | persons, dist = "negbin", EM =

Dear all, I want to build a model in R based on animal collection data, that look like the following Nr Village District Site Survey Species Count 1 AX A F Dry B 0 2 AY A V Wet A 5 3 BX B F Wet B 1 4 BY B V Dry B 0 Each data point shows one collection unit in a certain Village, District, Site, and Survey for a certain Species. 'Count' is the number of animals collected in that

Hello, I am having a problem with the zero-inflated negative binomial (package pscl). I have 6 sites with plant populations, and I am trying to model the number of seeds produced as a function of their size and their site. There are a lot of zero's because many of my plants get eaten before flowering, thereby producing 0 seeds, and that varies by site. Because of that and because the

Dear R crew: I think I am in the right mailing list. I have a very simple dataset consisting of two variables: cestode intensity and chick size (defined as CAPI). Intensity is clearly overdispersed, with way too many zeroes. I'm interested in looking at the association between these two variables, i.e. how well does chick size predict tape intensity? I fit a zero inflated negat. binomial

Dear All! It's now weeks that I'm going crazy with R, and as I'm a new user I now ask for help (also because I still have only a few days to finish..)... So shortly I describe you my Experiment in which I was looking for the decomposition of herbivore dung under different treatments: I made a box design experiment which is structured in the following way: I collected dung from 2

dear list! i have run several glm analysises to estimate a mean rate of dung decay for independent trials. i would like to compare these results statistically but can't find any solution. the glm calls are: dung.glm1<-glm(STATE~DAYS, data=o_cov, family="binomial(link="logit")) dung.glm2<-glm(STATE~DAYS, data=o_cov_T12, family="binomial(link="logit")) as

Dear R-Experts, Dear friends of dung. I have a statistical Problem, to which nobody I asked could give me an answer. Maybe you can. I was in the African-Savanna and made a Dung-Monitoring. This means I walked randomly over the field and for every Dung-Event I found I noted following parameters: Species (Waterbuck, Giraffe, Reedbuck); Age-Category (less than a week, more than a week & less

Hi R helpers, I have a problem on a zero-inflated binomial distribution. I have many regressions and few observations for which I wanted to apply the LASSO regression. Is there a package that allows the ZIB-Lasso? Thank you very much! [[alternative HTML version deleted]]

I'm trying to use the inflated binomial distribution of zeros (since 75% of the values are zeros) in a randomized block experiment with four quantitative treatments (0, 0.5, 1, 1.5), but I'm finding it difficult, since the examples available in VGAM packages like for example, leave us unsure of how it should be the data.frame for such analysis. Unfortunately the function glm does not have

Dr. Flom, I was searching the web for any examples of one-inflated negative binomial regression, and ran across your post. Fittingly, I am working on the analysis of data from the NIDA Cooperative Agreement where I had the pleasure of working with Sherry Deren and other folks at NDRI. NBR does a poor job of modeling number of sex partners. (I am using Stata.) Did you have any luck modeling a

Hello I am interested in Poisson or (ideally) Negative Binomial regression with an inflated number of 1 responses I have seen JK Lindsey's fmr function in the gnlm library, which fits zero inflated Poisson (ZIP) or zero inflated negative binomial regression, but the help file states that for ' Poisson or related distributions the mixture involves the zero category'. I had thought

Dear R-Experts. I collected different datas about Nitrogen content (mg/ml) in Dung. The dung was eighter fresh (day=0) or had different ages (15,29,47) to observe nutrient changes over time. Now I like to draw a boxplot. boxplot(nmgml~day) abline((nmgml~day) The Problem is, that the boxplot considers the day values as groups and not as time series (neighter when the days are numeric or

Dear list, I would like to know: 1. After I have used the R code (http://pscl.stanford.edu/zeroinfl.r) to fit a zero-inflated negative binomial model, what criteria I should follow to compare and select the best model (models with different predictors)? 2. How can I compare the model I get from question 1 (zero-inflated negative binomial) to other models like glm family models or a logistic

Here is a summary of responses to my original email (see my query at the bottom). Thank you to Achim Zeileis , Anders Nielsen, Pierre Kleiber and Dave Fournier who all helped out with advice. I hope that their responses will help some of you too. ***************************************** Check out glm.nb() from package MASS fits negative binomial GLMs.

Dear list, I would like to fit a negative binomial regression model as described in "Byers AL, Allore H, Gill TM, Peduzzi PN., Application of negative binomial modeling for discrete outcomes: a case study in aging research. J Clin Epidemiol. 2003 Jun;56(6):559-64" to my data in which the response is count data. There are also 10 predictors that are count data, and I have also 3