similar to: GAM hurdle models

Dear list, I am using the hurdle model for modelling the habitat of rare fish species. However I do get an error message when I try to model my data: > test_new1<-hurdle(GALUMEL~ depth + sal + slope + vrm + lat:long + offset(log(haul_numb)), dist = "negbin", data = datafit_elasmo) Error in hurdle(GALUMEL ~ depth + sal + slope + vrm + lat:long + offset(log(haul_numb)), :

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

Dear All, I am using the function vuong from pscl package to compare 2 non nested models NB1 (negative binomial I ) and Zero-inflated model. NB1 <-  glm(, , family = quasipoisson), it is an object of class: "glm" "lm" zinb <- zeroinfl( dist = "negbin") is an object of class: "zeroinfl"   when applying vuong function I get the following: vuong(NB1,

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

Dear fellows, I'm trying to extract the AIC statistic from a GLM model with quasipoisson link. The formula I'm referring to is AIC = -2(maximum loglik) + 2df * phi with phi the overdispersion parameter, as reported in: Peng et al., Model choice in time series studies os air pollution and mortality. J R Stat Soc A, 2006; 162: pag 190. Unfortunately, the function logLik

Hi all-- We are planning an intervention study for adolescent alcohol use, and I am planning to use simulations based on a hurdle model (using the hurdle() function in package pscl) for sample size estimation. The simulation code and power code are below -- note that at the moment the "power" code is just returning the coefficients, as something isn't working quite right. The

Hello, I'm using pscl to run a hurdle model. Everything works great until I get to the point of making predictions. All of my "count" predictions are lower than my actual data, and lower than the "response" predictions, similar to the issue described here ( https://stat.ethz.ch/pipermail/r-help/2012-August/320426.html) and here (

This request is related to the following post from last year: https://stat.ethz.ch/pipermail/r-help/2011-June/279752.html After reading the thread, the idea is still not clear. I have fitted a model using HURDLE from the PSCL package. I am trying to get marginal effects / slopes by multiplying the coefficients by the mean of the marginal effects (I think this is right). To my understanding, this

Dear Listers, I have a general statistical question. Are hurdle logit-poisson model and posson model nested? Thank you so much?

I would like to test in R what regression fits my data best. My dependent variable is a count, and has a lot of zeros. And I would need some help to determine what model and family to use (poisson or quasipoisson, or zero-inflated poisson regression), and how to test the assumptions. 1) Poisson Regression: as far as I understand, the strong assumption is that dependent variable mean = variance.

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

I am interested in utilizing this so called "double hurdle" model in my study. We can write the model in the following way: if (z'a + u > 0 & x'b + e > 0) y = x'b + e, else y = 0 In the model, consumption y is the (left-) censored dependent variable. e and u are the normally distributed error terms. z'a is the participation equation and x'b is the

Dear all.. I have a respon variable y. Predictor variable are x1, x2, x3, x4, x5 (1) What is the syntax to get paramater estimation of ZINB Model by Newton Raphson (not BFGS) (2) What syntax to plot probability of observed & predicted of ZINB Thx. Regards Krist. [[alternative HTML version deleted]]

Hi! I am a psychologist who suspects that the only sensible way to analyse a particular data set is to use generalised linear mixed models. I am hoping that someone might be able to point me in the right direction to find some very practical hands on documentation that might be able to talk me through actually doing such an analysis? So far in my searches the most useful document I have turned

I have some questions regarding Zero Inflation Poisson models. I am using count data to analyze abundance trends of salamanders. However, I have surveys which differ in the amount of effort (i.e. the number of people searching and amount of time - I am using a museum database so not all surveys were conducted by me). Therefore I need to account for the effort. If change the count (response

Dear R users, I want to apply zero inflated models with continuous and categorical variables and I used pscl package from R and the zeroinf() function. My question are the follow: a) The value of fitted.values is mu or (1-p)*mu? where p is the probability of zero came form a zero point mass b) If mu is zero, how do i know if it is a zero from the zero point mass or from the count process?

Hi, I have two questions. First, I'd like to compare a ZINB model to a negativ binomial model with the Vuong test, but I can't find how to performe it from the zicount package. Does a programm exist to do it ? Second, I'd like to know in which cases we have to use a double hurdle model instead of a zero inflated model. Many thanks, St??phanie Payet REES France R??seau

Dear R users, I have been using the zicounts package (verson 1.1.4) in R (version 2.4.1). I have been fitting zero inflated Poisson regressions to model the number of trips made by a household. Whilst I can get the best fit parameter set from zicounts, I can't get the package to return the fitted values for the model. I have attempted to calculate the fitted values from the optimal

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 all, I am using the hurdle model for fitting my count data using the pscl package which is working fine. However, I am stuck with the problem of calculating the percent correctly predicted (PCP) zeros for hurdle model. The method I am trying to use to achieve this is 'hitmiss' in the pscl package (ref: http://www.inside-r.org/packages/cran/pscl/docs/hitmiss). When I do: >