similar to: Can R do zero inflated gamma regression?

Dear R and lme4 users- I am trying to fit a mixed-effects model, with the glmer function in lme4, to right-skewed, zero-inflated, non-normal data representing understory grass and forb biomass (continuous) as a function of tree density (indicated by leaf-area). Thus, I have tried to specify a Gamma distribution with a log-link function but consistently receive an error as follows: >

Dear, I'm very new to R Gui and I have to make an assignment on Gamma Regressions. Surfing on the web doesn't help me very much so i hope this forum may be a step forward. The question sounds as follows: The data set is in the library MASS first install library(MASS) then type data(mammals) attach(mammals) Assignment: Fit the gamma model and lognormal model for the mammals data.

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

I'm trying to run a zero-inflated regression for a continuous response variable in R. I'm aware of a gamlss implementation, but I'd really like to try out this algorithm by Dale McLerran that is conceptually a bit more straightforward. Unfortunately, the code is in SAS and I'm not sure how to re-write it for something like nlme (if at all possible - with conditions etc). Does

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.

Hi, I am trying to replicate Lambert (1992)'s simulation with zero-inflated Poisson models. The citation is here: @article{lambert1992zero, Author = {Lambert, D.}, Journal = {Technometrics}, Pages = {1--14}, Publisher = {JSTOR}, Title = {Zero-inflated {P}oisson regression, with an application to defects in manufacturing}, Year = {1992}} Specifically I am trying to recreate Table 2. But my

Sub: using glmer to fit a mixed-effects model with gamma-distributed response variable Hello, I'm currently trying to fit a mixed effects model , i.e.: > burnedmodel1.2<-glmer(gpost.f.crwn.length~lg.shigo.av+dbh+leaf.area+ bark.thick.bh+ht.any+ht.alive+(1|site/transect/plot), family=gaussian, na.action=na.omit, data=rws30.BL) If I run this code, I get the error below: Error:

Dear R Users, I am trying to obtain a best-fit analytic distribution for a dataset with 11535459 entries. The data range in value from 1 to 300000000. I use: fitdistr(data, "gamma") to obtain mle's for the parameters. I get the following error: Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : non-finite finite-difference value [1] And the following warnings:

How to adapt this piece of code but for: - gamma distribution - 3 parameter log normal More specifically, where can I find the specification of the parameter (lmom) for pelgam() and pelln3()? Lmom package info just gives: pelgam(lmom), lelln3(lmom), where lmom is a numeric vector containing the L-moments of the distribution or of a data sample. # Draw an L-moment ratio diagram and add a line for

Dear Madam/Sir This may be quite a long shot... By way of intro, I am a masters student in actuarial science at the University of Cape Town, and I am doing a project in R on some healthcare cost data. During my coding in R I encountered an error message, which I then googled, but I am still unable to resolve the issue. I would like to please ask if and how it is possible to resolve the problem

Dear list, I am running some zero inflated models and would like to know what the deviance of the models. Unlike running a normal GLM where the deviance is displayed in the summary all that is displayed in a summary of the zero inflated model is the log likelihood. I hope this isn't a read the manual question, and if it is I apologize for wasting your time, but if you could still send me 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 netters, Sorry for cross-posting this question. I am sure R-Help is not a research methods discussion list, but we have many statisticians in the list and I would like to hear from them. Any function/package in R would be able to deal with the problem from this researcher? ---------- Forwarded message ---------- From: Heidi Bertels Date: Tue, Jun 5, 2012 at 4:31 PM Subject: How to best

Sorry bit of a Newbie question, and I promise I have searched the forum already, but I'm getting a bit desperate! I have over-dispersed, zero inflated data, with variance greater than the mean, suggesting Zero-Inflated Negative Binomial - which I attempted in R with the pscl package suggested on http://www.ats.ucla.edu/stat/R/dae/zinbreg.htm However my data is non-integer with some pesky

Hello, I have count data that illustrate the presence or absence of individuals in my study population. I created a grid cell across the study area and calcuated a count value for each individual per season per year for each grid cell. The count value is the number of time an individual was present in each grid cell. For illustration my data columns look something like this and are repeated for

Hi just wondering if there is a package that can get the maximum likelihood or method of moments estimator for distributions with censored data? The distributions I'm interested in are: Exponential, pareto, beta, gamma and lognormal. -- View this message in context: http://www.nabble.com/MLE-for-censored-distributions-in-R-tp15022863p15022863.html Sent from the R help mailing list archive at

Hello R users, Doing My PhD I collected count data which I believe is zero inflated. I have run a statistical model with lmer and family=poisson and got summary(model)@sigma=1 so I believe there is no overdispertion. I would like to use the fmr function from the 'gnlm' library but I just cannot figure out from the examples in the help page and some forums out there how to convert the lmer

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 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

Dear UseRs: I have simulated data from a zero-inflated Poisson model, and would like to use a package like zicounts to test my code of fitting the model. My question is: can I use zicounts directly with the following simulated data? Create a sample of n=1000 observations from a ZIP model with no intercept and a single covariate x_{i} which is N(0,1). The logit part is logit(p_{i})=x_{i}*beta