similar to: pscl package and hurdle model marginal effects

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 (

I ran a negative binomial logit hurdle model and am now trying to plot the effects of a continuous predictor variable (the only variable in my model) on the count and zero component and the overall mean response. I'm confused because for some values, the predicted overall mean is higher than the mean of the non-zero counts (range of predicted overall means=2.2-11.0; range of non-zero count

It seems that MASS suggest to judge on the basis of sum(residuals(mode,type="pearson"))/df.residual(mode). My question: Is there any rule of thumb of the cutpoiont value? The paper "On the Use of Corrections for Overdispersion" suggests overdispersion exists if the deviance is at least twice the number of degrees of freedom. Are there any further hints? Thanks. -- Ronggui

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

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: >

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 have been using mgcv to run GAM hurdle models, analyzing presence/absence data with GAM logistic regressions, and then analyzing the data conditional on presence (e.g. without samples with no zeros) with GAMs with a negative binomial distribution. It occurs to me that using the negative binomial distribution on data with no zeros is not right, as the negative binomial allows zeros.

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?

Hello, I'm using GLMM on the pscl package and i'm not getting the AIC on the summary. The code i'm using is (example) : mmall3 <-glmmPQL(allclues ~ cycloc + male, data=dados, family=poisson, random=~1|animal/idfid) and the results: Linear mixed-effects model fit by maximum likelihood Data: dados AIC BIC logLik NA NA NA Random effects: Formula: ~1 | animal

Hi, This may be such a general question that my searches are just failing. I installed the pscl lib, all appears fine, installed it several different ways to be sure, but I am getting: Error: could not find function "zeroinfl" I double checked my spelling of the function and that it had not been evolved out of the package. It is in the same location as the other libraries that are

Dear list, I'm currently trying to develop a model to assess clam yield potential in a lagoon. I'm using the zeroinfl function of the pscl package to fit a Zero-inflated negative binomial model, given the high occurrence of zero counts. I don't understand from the sentence in the pscl guide "Zero-inflated count models are a type of two-component mixture model, with a component

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

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 =

Hi all, I'm trying to run some monte carlo simulation for my roll call data using the ideal() function, which resides in the pscl package. However, I'm receiving an error message that I don't understand. Error in ideal(a, maxiter = 1000, thin = 10, burnin = 50, store.item = TRUE, : NA/NaN/Inf in foreign function call (arg 13) my code is simple the following: > m_a <-

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)), :

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 list, I'm currently analyzing some count data using a hurdle model. I've used the rcspline.eval function in the Hmisc-library to contruct the spline terms for the regression model, and what I want in the end is the ability to compute coefficients and confidence intervals for different changes in the smooth function as well as plotting the smooth function along with the

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]]

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