similar to: Negative binomial regression for count data

Dear list, I would like to know: 1. After I have used the R code (http://pscl.stanford.edu/zeroinfl.r) to fit a zero-inflated negative binomial model, what criteria I should follow to compare and select the best model (models with different predictors)? 2. How can I compare the model I get from question 1 (zero-inflated negative binomial) to other models like glm family models or a logistic

Dear R users, I am trying to fully understand the difference between estimating overdispersion with glm.nb() from MASS compared to glm(..., family = quasipoisson). It seems that (i) the coefficient estimates are different and also (ii) the summary() method for glm.nb suggests that overdispersion is taken to be one: "Dispersion parameter for Negative Binomial(0.9695) family taken to be

Dear list, I have been comparing the outputs of two packages for latent class regression, namely 'flexmix', and 'mmlcr'. What I have noticed is that the flexmix package appears to come up with a much better fit than the mmlcr package (based on logLik, AIC, BIC, and visual inspection). Has anyone else observed such behaviour? Has anyone else been successful in using the mmlcr

All: I am new to R and would like your help in identifying the appropriate process to follow in order to modify the output from an existing package. I've had difficulty finding an answer online, perhaps because I am using incorrect terminology. A package that I am using (mmlcr) invokes another package (multinom). An output of multinom is the standard errors, but this output is not

I am new to mmlcr and am working on a latent class mixture model attempting to identify the trajectory and number of classes that best describes my data. I am able to find model parameters such as degrees of freedom, loglikelihood, and BIC. For example, here is a cubic 3-class model I am using. mmlcr1 <- mmlcr(outer = ~ 1 | ID, components = list(list(formula = Score ~ poly(wave,3), class =

Hi the list. Is there a bug in mmlcr package ? The following code does not compile: mmlcrTest <- function(dataW){ dataL <- reshape(dataW,idvar="id",timevar="T",varying=list("T0","T1","T2"),direction="long",v.names="score") resultR <- mmlcr(outer= ~ 1 | id, components = list(list(formula =

Greetings I'm a relatively new R user and I'm trying to build a latent class model. I've used the 'R Site Search' and it appears there's not much dialogue on these packages On mmlcr, I've gotten it working, but not sure if I'm using it correctly. On flexmix, I can only seem to get results for one class. I'm attaching my code below - if anyone

Dear R users, I'm wanting to sample from the negative binomial distribution using the rnegbin function from the MASS library to create artificial samples for the purpose of doing some power calculations. However, I would like to work with samples that come from a negative binomial distribution that includes only values greater than or equal to 1 (a truncated negative binomial), and I

Hi I am running the following code: sym <- c(sym1,sym2,sym4) lifedxm <- c("O-BD","O-WELL","O-UNI") life <- c(lifedxm,lifedxm,lifedxm) tp <- c("TP-ANY","TP-ANY", "TP-ANY", "TP-SUB", "TP-SUB", "TP-SUB", "TP-CLIN" , "TP-CLIN", "TP-CLIN") data <-

I have some questions about the "Continious Repo". Does the "Continious Repo" replace the Centos-Base/Updates repo? Is the "Continious Repo" in a standard place on the CentOS mirrors? Ie. can I just add another line to my rsync script to get this repo? It is not possible for me to just update directly off the net (I have a slow and unreliable network connection)

I'm having some difficulty with graphing outputs of a GLM model I've been working. I have count data for both my predictor (only 1) and response variables, and I have pseudoreplication which I've modeled as a random effect. The odTest() from pscl:: indicated that the negative binomial distribution fit better than Poisson, and I then proceeded by estimating theta from glm.nb. My

I want to generate 1,000 random samples of sample size=1,000 from ZINB. I know there is a rnegbin() to generate random samples from NB, and I know I can use the following process: do i=1 to 1000 n=0 do i=1 to 1000 if runi(1)>0.1 then x(i) = 0; else x(i)=rnegbin(); n=n+1; if n>1000 then stop; end; output; end; Anybody can help me out with the R code? Thanks very much ahead of time.

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 R-Users! I fitted a negative binomial distribution to my count data using the function glm.nb() and obtained the calculated parameters theta (dispersion) and mu. I would like to simulate values from this negative binomial distribution. Looking at the function rnbinom() I was looking at the relationship between the two possible parametrizations of the negative binomial and found that for this

All, I have some data on parasites on apple leaves and want to do a goodness of fit test to a Poisson distribution. This seems to do it: mites <- c(rep(0,70), rep(1,38), rep(2,17), rep(3,10), rep(4,9), rep(5,3), rep(6,2), rep(7,1)) tab <- table(mites) NSU <- length(mites) N <-

Hi All, I am running a scatter plot and trying to add a best fit line. I use an abline function, but get no line drawn over the points. I also get no error. I arm using V 2.10.0 on Windows 7. Here is my code, including the SAS transport file import: require (foreign) require (chron) require (Hmisc) require (lattice) clin <- sasxport.get("y:\\temp\\subset.xpt") attach(clin)

I downloaded: http://www.bioconductor.org/data/metaData/hgu95av2_1.7.0.tar.gz described as: Package: hgu95av2 Title: A data package containing annotation data for hgu95av2 Version: 1.7.0 Created: Wed Jan 12 16:57:23 2005 Author: Lin,Chenwei Description: Annotation data file for hgu95av2 assembled using data from public data repositories Maintainer:

Dear List, Does anyone have experience using mmlcr Package? I am working on a longitudinal dataset. The model fitting is fine. However, I had a problem with the command "plot.mmlcr". It seems to me it can not plot a very nice trajectory plot for longitudinal dataset. I wonder whether somebody can share the experience, i.e. how to make a nice trajectory plot for longitudinal data.

How can I eliminate the overdispersion for binary data apart the use of the quasibinomial? help me Eva Iannario ------------------------------------------------------ Passa a Infostrada. ADSL e Telefono senza limiti e senza canone Telecom http://click.libero.it/infostrada11gen07

Hello, I'm a grad student in the Intelligent Transportation Systems lab at Portland State Univ. in Portland, OR, USA. I'm trying to learn the basics of R to run a negative binomial in the near future, and so I ran a test regression on roadway crash data obtained from "Statistical and Econometric Methods for Transportation Data Analysis" by Washington et al (p. 250). I ran the