similar to: Convergence problem with zeroinfl() and hurdle() when interaction term added

Good afternoon, I?m in need of an advice regarding a proper use of glm.nb, zeroinfl or hurdle with my dataframe. I can not provide a self-contained example, since I need an advice on this current dataset and its ?contradictory? results. So.... i have a dataset which contains 1309 cases and 11 variables, highly right-skewed and heavily zeroinflated (with over 1100 cases that have 0 value

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 =

Dear R-Helpers, Can anyone please help me to interpret warning messages from zeroinfl (package pscl) while fitting a zero inflated negative binomial model? The console reports convergence and the parameters seam reasonable, but these <<Warning messages: 1: algorithm did not converge in: glm.fit(X, Y, family = poisson()) 2: fitted rates numerically 0 occurred in: glm.fit(X, Y, family =

Dear list, I'm wondering if anyone can help me calculate the deviance of either a zeroinfl or hurdle model from package pscl? Even if someone could point me to the correct formula for calculating the deviance, I could do the rest on my own. I am trying to calculate a pseudo-R-squared measure based on the R^{2}_{DEV} of [1], so I need to be able to calculate the deviance of the full and null

Hi, I am wondering if there is an easy way to access the hat matrix for zeroinfl and hurdle objects in the pscl library? Thanks, Chris [[alternative HTML version deleted]]

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, 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

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

Dear Mr. or Ms.,   I used the R-software to run the zero-inflatoin negative binomial model (zeroinfl()) .   Firstly, I introduced one dummy variable to the model as an independent variable, and I got the estimators of parameters. But the results are not satisfied to me. So I introduced three dummy variables to the model. but I could not get the results. And the error message is

I have hundreds of megabytes of price data time series, and perl scripts that extract it to tab delimited files (I have C++ programs that must analyse this data too, so I get Perl to extract it rather than have multiple connections to the DB). I can read the data into an R object without any problems. thedata = read.csv("K:\\Work\\SignalTest\\BP.csv", sep = "\t", header =

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

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

The problem: I would like to translate the Octave algorithm in griddata.m to R. Within the griddata algorithm calls are made to the Delaunay function. For the R translation I have found delaunayn within the "geometry" package and also the deldir package. Both do similar things but give slightly different results depending on the input. The question is, what is making the results for the

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

Hello, On Mac OS X, certain Aqua/Quartz UI functionality requires an application to be launched from within an app bundle, or (alternatively) requires a Carbon application with a resource fork. Playing with the wxWidgets distribution, I discovered that it is quite easy and transparent to make such a Carbon app from (I guess) any command line application. When applied to the R executable called

Hello, On Mac OS X, certain Aqua/Quartz UI functionality requires an application to be launched from within an app bundle, or (alternatively) requires a Carbon application with a resource fork. Playing with the wxWidgets distribution, I discovered that it is quite easy and transparent to make such a Carbon app from (I guess) any command line application. When applied to the R executable called

Hi all, I'm handling massive data.frames and matrices in R (30000 x 400). In the 1st column, say, I have 0s and 1s indicating rows that matter; other columns have probability values. One simple task I would like to do would be to get the column mean values for signaled rows (the ones with 1) As a very fresh "programmer" I have build a simple function in R which should not be very

Hello r-experts, I sure could us a little help. I have an ever updating text file with timestamped data in it. I can reformat in anyway I want if need be but currently I have chosen to make columns of date, time and measuresed value (comma delimeted and with the dates and times in quotes to interpret them as strings). Here is a small section of my text data file:

Hi, A simple question I hope. I wish to add a single vertical line to a plot with several density plots. Here is a simplified example. ############ thedata <- data.frame(x1=rnorm(100,1,1),x2=rnorm(100,3,1)) #create data thedata.m<-melt(thedata) densityplot(~value, thedata.m, groups=variable,auto.key=list(columns=2)) #this gives the two density plots ######### what I wish now is to add a

Hi all, I have been trying to use zeroinfl() with the pscl package with R version 2.1.1. and with the newest versions of the contrib packages compatible with R 2.1.1. I have read the examples, the vignette and all the posts relating to zeroinfl() but I am still confused as to how to structure the model. Here is a small example; the error message is the same for big data sets