similar to: Zero inflated negative binomial

Hello, I have a data frame with 1425 observations, 539 of which are zeros. I am trying to fit the following ZINB: f3<-formula(Nbr_Abs~ Zone * Year + Source) ZINB2<-zeroinfl(f3, dist="negbin", link= "logit", data=TheData, offset=log(trans.area), trace=TRUE) Zone is a factor with 4 levels, Year a factor with 27 levels, and Source a factor with 3 levels. Nbr_Abs is counts

I'm trying to fit a zero-inflated poisson model using zeroinfl() from the pscl library. It works fine for most models I try, but when I include either of 2 covariates, I get an error. When I include "PopulationDensity", I get this error: Error in solve.default (as.matrix(fit$hessian)) : system is computationally singular: reciprocal condition number = 1.91306e-34 When I

Dear all, I noticed the following while playing around with fitting log-linear models to contingency tables using R 1.8.1, but the problem also exists under R 1.9.0. A reproducible example uses the following contingency table: > library(MASS) > data(quine) > tmp <- with(quine, expand.grid(Eth=levels(Eth), Sex=levels(Sex), + Lrn=levels(Lrn), Age=levels(Age)))

Hi, I am currently running the following negative binomial GLM: glm89.nb <- glm.nb(AvGUD ~ Year*Trt*Micro + (0 + Micro/Trt/Year)) where Year has 3 levels, Trt has 2 levels, and Micro has 3 levels. >From what I have read the above model has a 3 way interaction (Year*Trt*Micro), and Micro is nest within Trt and Trt is nested with Year (0 + Micro/Trt/Year). I was hoping someone could

All, The code below is for a pseudo dataset of repeated measures on patients where there is also a treatment factor called "drug". Time is treated as categorical. What code is necessary to test for a treatment effect at a single time point, e.g., time = 3? Does the answer matter if the design is a crossover design, i.e, each patient received drug and placebo? Finally, what would

Are you referring to the zeroinfl() function in the countreg package? If so, I think predict(fm_zinb2, type = "zero", newdata = some.new.data) will give you pi for each combination of covariate values that you provide in some.new.data where pi is the probability to observe a zero from the point mass component. As to your second question, I'm not sure that's possible, for any

Dear R crew: I am sorry this question has been posted before, but I can't seem to solve this problem yet. I have a simple dataset consisting of two variables: cestode intensity and chick size (defined as CAPI). Intensity is a count and 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

Dear all, I'm using zeroinfl() from the pscl-package for zero inflated Poisson regression. I would like to calculate (aproximate) prediction intervals for the fitted values. The package itself does not provide them. Can this be calculated analyticaly? Or do I have to use bootstrap? What I tried until now is to use bootstrap to estimate these intervals. Any comments on the code are welcome.

I am running a zero inflated regression using the zeroinfl function similar to the model below: fm_zinb2 <- zeroinfl(art ~ . | ., data = bioChemists, dist = "poisson") summary(fm_zinb2) I have three questions: 1) How can I obtain a value for the parameter pie, which is the fraction of the population that is in the zero inflated model vs the fraction in the count model? 2) For

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 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, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Dear R users, I'm trying to do betareg on my dataset. Dependent variable is not normally distributed and is proportion (of condom use (0,1)). But I'm having problems: gyl<-betareg(cond ~ alcoh + drug, data=results) Error in optim(par = start, fn = loglikfun, gr = gradfun, method = method, : initial value in 'vmmin' is not finite Why is R returning me error in optim()? What

Hi David: In looking at your original post it is a bit difficult to ascertain exactly what your null hypothesis was. That is, you want to assess whether there is a treatment effect at time 3, but compared to what. I think your second post clears this up. You should refer to pages 224- 225 of Pinhiero and Bates for your answer. This shows how to specify contrasts. > -----Original Message-----

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

Hi all, First post for me here, but I have been reading on the forum for almost two years now. Thanks to everyone who contributed btw! I have a dataset of 4000 observations of count of a mammal and I am trying to predict abundance from a inflated-zero model as there is quite a bit of zeros in the response variable. I have tried multiple options, but I might do something wrong as every

Dear all, A question related to the following has been asked on R-help before, but I could not find any answer to it. Input will be much appreciated. I got an unexpected sign of the "slope" parameter associated with a covariate (diam) using zeroinfl(). It led me to compare the estimates given by zeroinfl() and hurdle(): The (significant) negative estimate here is surprising, given

Hello, I?m using zero-inflated Poisson regression via the zeroinfl() function to analyze data on seed-set of plants, but for some reason, I don?t seem to be getting the output for all three levels of my two categorical predictors. More about my data and model: My response variable is the number of viable seeds (AVInt), and my two categorical predictors are elevation (Elev) and Treatment

Dear R-helpers, running a zeroinflated model of the following type: zinb = zeroinfl(count=response ~., x = ~ . - response, z = ~. - response, dist = "negbin", data = t.data, trace = TRUE) generates the following message: Zero-Inflated Count Model Using logit to model zero vs non-zero Using Negative Binomial for counts dependent variable y: Y 0 1 2 3 359 52 7 3 generating

Hello all: I have a question regarding the fitted.values returned from the zeroinfl() function. The values seem to be nearly identical to those fitted.values returned by the ordinary glm(). Why is this, shouldn't they be more "zero-inflated"? I construct a zero-inflated series of counts, called Y, like so: b= as.vector(c(1.5, -2)) g= as.vector(c(-3, 1)) x <- runif(100) # x