similar to: negative binomial predicted probabilities

Hi All, I have a dataset, longitudinal in nature, each row is a 'visit' to a clinic, which has numerous data fields and a count variable for the number of 'events' that occurred since the previous visit. ~50k rows, ~2k unique subjects so ~25 rows/visits per subject, some have 50 some have 3 or 4. In STATA there is an adjustment for the fact that you have multiple rows per

Dear Clara, Thanks for the reply. I am forwarding your message to the list, ok. When I wrote was a way of get further information to help the helpers. happy holidays, milton ---------- Forwarded message ---------- From: Clara Brück <clara_brueck@web.de> Date: 2009/12/30 Subject: Re: [R] Negbin Error Warnings To: milton ruser <milton.ruser@gmail.com> Dear Milton, Thanks for

Hi, I ran a negative binomial regression (NBR) using the Zelig-package and the negbin model. When I then try to use the simumlation approach using the setx () and sim() functions to calculate expected values and first difference for different levels of one of my independent variables, I get 50 errors warnings, telling me that the calculation rpois produced NAs. However, the data I use

Hello, I am having some difficulty converting my gam code to a correct gamm code, and I'm really hoping someone will be able to help me. I was previously using this script for my overdispersed gam data: M30 <-gam(efuscus~s(mic, k=7) +temp +s(date)+s(For3k, k=7) + pressure+ humidity, family=negbin(c(1,10)), data=efuscus) My gam.check gave me the attached result. In order to

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 =

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 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

Hi there, My question is about the 'theta' parameter in specification of a NB GAMM. I have fit a GAM with an optimum structure of: SB.gam4<-gam(count~offset(vol_offset)+ s(Depth_m, by=StnF, bs="cs")+StageF*RegionF, family=negbin(1, link=log), data=Zoop_2011[Zoop_2011$SpeciesF=='SB',]) However, this GAM shows heterogeneity in 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

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 =

I ran three logit models in R with the Zelig package and I'm trying to compute the predicted probabilities for a number of different values on the independent variable. My dep variable was accepted or decline and my indep variable is bid amount, and varies. So for a bid amount of 3, what's the expected probability of winning. For a bid amount of 5, what's the expected probability of

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

Hello list, I am not sure if the terminology that I am using here is widely used, however, I provide an example in the hopes that my problem will become clear. My basic problem is that I am unsure of how to 'constrain' my model estimates to reproduce the aggregate (by factor levels) observed dependent variable for a negative.binomial model. I realize this sounds rather vague, so I provide

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

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

I am a student who is doing empirical work for his thesis and trying to switch to R. I am familiar with Stata, and at the moment I am trying to replicate some of my previous work. I have a large unbalanced panel data set, observations for different countries between 1970 and 2007. My dependent variable is an overdispersed count. So far I have used fixed-effects negative binomial regression,

I've run an ordered logistic regression model in R with Zelig and am looking to calculate predicted probabilities. Zelig has a series of simple one line commands to generate the information I want on first differences and so forth. Unfortunately, I keep getting an error when running the zelig function and was wondering if there was a quick alternative for generating predicted probabilities for

Hi, I have been trying to fit an un-penalised gam in mgcv (in order to get more reliable p-values for hypothesis testing), but I am struggling to get the model to fit sucessfully when I add in a te() interaction. The model I am trying to fit is: gam(count~ s(x1, bs = "ts", k = 4, fx = TRUE) + s(x2, bs = "ts", k = 4, fx = TRUE) + te(x2, x3, bs =

Goal: use GEE or GLMM to analyze repeated measures data in R GEE problem: can?t find a way to do GEE with negative binomial family in R GLMM problem: not sure if I?m specifying random effect correctly Study question: Does the interaction of director and recipient group affect rates of a behavior? Data: Animals (n = 38) in one of 3 groups (life stages): B or C. Some individuals (~5)

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