similar to: Vuong test

Dear All, I am using the function vuong of the package pscl to compare 2 non nested glm models with a numeric response. I did the following m1<-glm(y ~x ,data=xxx) m2<-glm(y ~z , data=xxx) When calling the vuong function I get the following message: > vuong(m1,m2) Error in predprob.glm(m1) : your object of class glm is unsupported by predprob.glmyour object of class lm is unsupported

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

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 All, I am using the function vuong of the package pscl to compare 2 non nested glm models with a numeric response. I did the following m1<-glm(y ~x ,data=xxx) m2<-glm(y ~z , data=xxx) When calling the vuong function I get the following message: > vuong(m1,m2) Error in predprob.glm(m1) : your object of class glm is unsupported by predprob.glmyour object of class lm is unsupported

I want to compare the poisson and the zero-inflated poisson distribution on describing the data. So, after using the GLM and the ZEROINFL function, I used the Voung function to compare them. Here is my code: library(pscl) glm1 <- glm(nmer9_1[, 1] ~ 1, family = poisson) zip <- zeroinfl(nmer9_1[, 1] ~ 1) vuong(glm1, zip) However, R returns the following error message: "Error: cannot

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

Hi all, Cameron and Trivedi in their 1998 Regression Analysis of Count Data refer to NB1 and NB2 NB1 is the negative binomial model with variance = mu + (alpha * mu^1) yielding (1+alpha)*mu NB2 sets the power to 2; hence, variance = mu + (alpha*mu^2) I think that NB2 can be requested via negbin2<-glm(hhm~sex+age,family=quasi(var="mu^2",link="log")) Is

Dear all, I am modeling the incidence of recreational anglers along a stretch of coastline, and with a vary large proportion of zeros (>80%) have chosen to use a zero inflated negative binomial (ZINB) distribution. I am using the same variables for both parts of the model, can anyone help me with R code to compute overall marginal effects of each variable? My model is specified as follows:

Hello, I am working with zero inflated models for a current project and I am getting wildly different results from R's zeroinfl(y ~ x, dist="negbin") command and Stata's zinb command. Does anyone know why this may be? I find it odd considering that zeroinfl(y ~ x, dist="poisson") gives identical to output to Stata's zip function. Thanks, --david [[alternative

Dear Friends, My problem is related to how to measure probabilities from a probit model by changing one independent variable keeping the others constant. A simple toy example is like this Range for my variables is defined as follows y=0 or 1, x1 = -10 to 10, x2=-40 to 100, x3 = -5 to 5 Model output <- glim(y ~ x1+x2+x3 -1, family=binomial(link="probit")) outcoef <-

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 =

Can anyone explain to me why the AIC values are so different when using glm.nb and glm with a negative.binomial family, from the MASS library? I'm using R 1.8.1 with Mac 0S 10.3.4. >library(MASS) > dfr <- data.frame(c=rnbinom(100,size=2,mu=rep(c(10,20,100,1000),rep(25,4))), + f=factor(rep(seq(1,4),rep(25,4)))) > AIC(nb1 <- glm.nb(c~f, data=dfr)) [1] 1047 >

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]]

Buenas tardes, Estoy estimando un modelo binomial negativo de ceros inflados (ZINB) utilizando el comando zeroinfl() del paquete pscl. Al ejecutarlo me da el siguiente aviso: Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred ¿Sabéis que significa y si puedo usar el modelo aún con ese aviso? ¿Los coeficientes son fiables? Muchas gracias, Miriam

Dear R Users, When using zeroinfl() function to fit a Zero-Inflated Negative Binomial (ZINB) model to a dataset, the summary() gives an estimate of log(theta) and its standard error, z-value and Pr(>|z|) for the count component. Additionally, it also provided an estimate of Theta, which I believe is the exp(estimate of log(theta)). However, if I would like to have an standard error of Theta

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 =

Dear R-helpers, running a zeroinflated model of the following type: zinb = zeroinfl(count=response ~., x = ~ . - response, z = ~. - response, dist = "negbin", data = t.data, trace = TRUE) generates the following message: Zero-Inflated Count Model Using logit to model zero vs non-zero Using Negative Binomial for counts dependent variable y: Y 0 1 2 3 359 52 7 3 generating

Dear Mr.Zeileis & all. (1)     Thx for your reply. Yes, I am talk about the function zeroinfl() from the package "pscl". I want to use Newton Raphson to get parameter             estimation ZINB, so I try this: ----------------------------------------------------------------------------------------------------------------------------------         > zinb <- zeroinfl(y

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

Muchas gracias por la respuesta. He mirado y los coeficientes no son altos pero sí tengo una gran cantidad de ceros en la variable dependiente (más del 90%). Sin embargo, al incluir otro tipo de variables independientes no me da ese aviso, dejando la misma variable dependiente. ¿Cómo podría utilizar stan/rstan de forma sencilla para diagnosticar el modelo? Muchas gracias El Lun, 9 de Abril de