similar to: are hurdle logit-poisson model and posson model nested?

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)), :

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

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 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

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.

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 Mr. or Ms.,   I would like to use the R-software to run the zero inflated mixed model and hurdle mixed model. But I do not know how to do? Would you please tell me the code and data format?   I will be very appreciated if you can help me. Thank you very much.   Best regards,   Sincerely, Xiongqing Zhang [[alternative HTML version deleted]]

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

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

Hi all, First post for me here, but I have been reading on the forum for almost two years now. Thanks to everyone who contributed btw! I have a dataset of 4000 observations of count of a mammal and I am trying to predict abundance from a inflated-zero model as there is quite a bit of zeros in the response variable. I have tried multiple options, but I might do something wrong as every

Hello, I have a data frame with 1425 observations, 539 of which are zeros. I am trying to fit the following ZINB: f3<-formula(Nbr_Abs~ Zone * Year + Source) ZINB2<-zeroinfl(f3, dist="negbin", link= "logit", data=TheData, offset=log(trans.area), trace=TRUE) Zone is a factor with 4 levels, Year a factor with 27 levels, and Source a factor with 3 levels. Nbr_Abs is counts

Dear R-listers, I want to reperfrom a poisson distribution test that presented in a recent-published biological research paper (Plant Physiology 2008, vol 148, pp. 1189-1200). That test is about the occurrence number of a kind of gene in separate chromosomes. For instance: The observed gene number in chromosome A is 36. The expected gene number in chromosome A is 30. Then, the authors got a

Dear all I want to simulate a stochastic jump variance process where N is Bernoulli with intensity lambda0 + lambda1*Vt. lambda0 is constant and lambda1 can be interpreted as a regression coefficient on the current variance level Vt. J is a scaling factor How can I rewrite this avoiding the loop structure which is very time-consuming for long simulations? for (i in 1:N){ ... N <- rbinom(n=1,

Hi, Does anyone know of a function in R that can handle a fractional variable as the dependent variable? The catch is that the function has to be inclusive of 0 and 1, which betareg() does not. It seems like GLM might be able to handle the fractional logit model, but I can't figure it out. How do you format GLM to do so? Best, Rachael [[alternative HTML version deleted]]

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: >

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

Dear All, I am wondering about the fitting negative binomial(NB) hurdle model with random effect using REML estimation method in R. We can fit regular hurdle model without random effect using ML method as following. hurdle(pkg).... But, I couldn't figure out how I can fit NB hurdle model with random effect using REML in R. Please give me a tip. Thank you so much. Sincerely, SK

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

My reply seems not to have made it to either list 24 hours after I sent it, so I'm sending it again. Apologies if you get this twice (or 4 times if you're on both lists, or 6 times if you're John) Martyn -----FW: RE: [R] First (well second) CODA hurdle problem----- Date: Thu, 05 Aug 1999 09:40:01 +0200 (CEST) From: Martyn Plummer <plummer at iarc.fr> To: John Logsdon

Simplify your model. Does your TandemRepeat have a lot of levels? Or is your sample size very small? Alain Dear all, I am fitting a hurdle model in the following way: HNB <- hurdle(chro ~ as.factor(TandemRepeat)| as.factor(TandemRepeat), data =data_negbin_fin, dist = "negbin") But the std. error for log(theta) = NA Count model coefficients (truncated negbin with log link):