search for: countreg

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'...

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 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

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]]

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 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?

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 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

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

...y of Alaska Fairbanks Fairbankse, Alaska, USA Reference Zeileis, A., C. Kleiber, and S. Jackman, 2007. Regression models for count data in R. Technical Report 53, Department of Statistics and Mathematics, Wirtschaftsuniversit?t Wien, Wien, Austria. URL http://cran.r-project.org/doc/vignettes/pscl/countreg.pdf. Code `data` <- structure(list(D = c(4, 5, 12, 4, 9, 15, 4, 8, 3, 9, 6, 17, 4, 9, 6, 9, 3, 9, 7, 11, 17, 3, 10, 8, 9, 6, 7, 9, 7, 5, 15, 15, 12, 9, 10, 4, 4, 15, 7, 7, 12, 7, 12, 7, 7, 7, 5, 14, 7, 13, 1, 9, 2, 13, 6, 8, 2, 10, 5, 14, 4, 13, 5, 17, 12, 13, 7, 12, 5, 6, 10, 6, 6, 10, 4, 4,...

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

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 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 am sorry this question has been posted before, but I can't seem to solve this problem yet. I have a simple dataset consisting of two variables: cestode intensity and chick size (defined as CAPI). Intensity is a count and 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

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

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

...[R] Obtaining a value of pie in a zero inflated model >> (fm-zinb2) >> Message-ID: <02c6fe89-ccae-6c7c-c61e-f79cffad4358 at binghamton.edu> >> Content-Type: text/plain; charset="utf-8" >> >> 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 ze...