similar to: New package aod: Analysis of Overdispersed Data

Hi, When modeling with negbin from the aod package, parameters for a given count y | lambda~Poisson(lambda) with lambda following a Gamma distribution Gamma(r, theta) are estimated. The intercept is called phi. Some other parameters may be also be estimated from factors in the data: the estimates returned for all these would be in accordance with the Value listing in the negbin entry in the aod

I have tried glm.nb in the MASS package, but many models (I have 250 models with different combinations of predictors for fish counts data) either fail to converge or even diverge. I'm attempting to use the negbin function in the AOD package, but am unsure what to use for the "random" term, which is supposed to provide a right hand formula for the overdispersion parameter.

I have fitted the faults.data to glm.nb and to the function negbin from the package aod. The output of both is the following: summary(glm.nb(n~ll, data=faults)) Call: glm.nb(formula = n ~ ll, data = faults, init.theta = 8.667407437, link = log) Deviance Residuals: Min 1Q Median 3Q Max -2.0470 -0.7815 -0.1723 0.4275 2.0896 Coefficients:

Dear all, I am working with MS Windows XP Pro (latest update) and a pre-compiled version of R: > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 8.1 year 2003 month 11 day 21 language R I meet a strange problem with install.packages and update.packages (called from the menu), that did not occur until

I've submitted a new, and substantially enhanced, version (1.2-0) of the Rcmdr package to CRAN. Some highlights (from the CHANGES) file: o Added ability to import from Excel, Access or dBase files (contributed by Samir Messad, Renaud Lancelot and Matthieu Lesnoff). o Added ability to read data from the clipboard (suggested by Graham Smith). o Added "Data -> Active data

I've submitted a new, and substantially enhanced, version (1.2-0) of the Rcmdr package to CRAN. Some highlights (from the CHANGES) file: o Added ability to import from Excel, Access or dBase files (contributed by Samir Messad, Renaud Lancelot and Matthieu Lesnoff). o Added ability to read data from the clipboard (suggested by Graham Smith). o Added "Data -> Active data

I am getting the following erro rmessage in ordistep. I have a number of similarly structured datasets using ordistep in a loop, and the message only occurs for some of the datasets. I cannot include a reproducible sample - the specific datasets where this is occur ing are fairly large and there are several pcnm's in the rhs of the formula. thanks for any pointers that may allow me to

Hello, All: The vignette with the sos package used "upquote.sty", required for R Journal when it was published in 2009. Current CRAN policy disallows "upquote.sty", and I've so far not found a way to pass "R CMD check" with sos without upquote.sty. I changed sos.Rnw per an email exchange with Prof. Ripley without solving the problem; see below. The

Dear all I was willing to use argument 'exclude' in function xtabs to remove some levels of factors (xtabs help page says '"exclude: a vector of values to be excluded when forming the set of levels of the classifying factors"). I tried: > mydata <- data.frame( treatment = c("B", "A", "C", "C", "B",

I am creating habitat selection models for caribou and other species with data collected from GPS collars. In my current situation the radio-collars recorded the locations of 30 caribou every 6 hours. I am then comparing resources used at caribou locations to random locations using logistic regression (standard habitat analysis). The data is therefore highly autocorrelated and this causes Type

I've been trough the R documentation for about half an hour and it's not clear to me how to do this: I need to format to character a series of integers from 1 to 1000, and I like them to look like "0001" "0002", "0059", "0123" and so on. Padded with zeroes to have four digits. Cheers! Mario. r-help-request at r-project.org wrote: > Send

Edward, you also can use the package aod on CRAN, see the help page of the function negbin. Best Matthieu An example: > library(aod) > data(dja) > negbin(y ~ group + offset(log(trisk)), ~group, dja, fixpar = list(4, 0)) Negative-binomial model ----------------------- negbin(formula = y ~ group + offset(log(trisk)), random = ~group, data = dja, fixpar = list(4, 0))

Dear all, I just installed R 2.5.0alpha and noticed new warnings related to the extractor "$" when using contributed packages. For instance: > library(RODBC) Warning message: $ operator is not valid for atomic vectors, returning NULL > library(aod) Package aod, version 1.1-18 > data(orob2) > m1 <- betabin(cbind(y, n-y) ~ 1, random = ~ 1, data = orob2) >

Hello R users: I am fairly new to R and am trying to figure out how to compute an intraclass correlation (ICC) and/or design effect for binary data? More specifically, I am trying to determine the amount of clustering in a data set - that is, whether certain treatment programs tend to work with more or less severe clients. The outcome variable is dichotomous (low severity / high severity)

I've been poking around with GLMs (on which I am *not* an expert) on behalf of a student, particularly binomial (standard logit link) nested models with overdispersion. I have one possible bug to report (but I'm not confident enough to be *sure* it's a bug); one comment on the general inconsistency that seems to afflict the various functions for dealing with overdispersion in GLMs

I am currently using AsteriskNow v2. What I am trying to accomplish is having all calls from an area code go directly to the person responsible for that area. While searching for a solution for this I did come across a post that had a few examples. So Josh at extension 1902 would receive all calls from the 808 area code. exten => s,1,GotoIf($${CALLERIDNUM:0:3}" = "808?1902|1)

Dear all, I have tried to modify the code of MASS:::stepAIC.default(), dropterm() and addterm() to use AICc instead of AIC for model selection. The code is appended below. Somehow the calculations are still not correct and I would be grateful if anyone could have a look at what might be wrong with this code... Here is a working example: ## require(nlme) model1=lme(distance ~ age + Sex, data =

Dear R-users, Earlier this year I posted a message to this list regarding negative binomial mixed models in R. It was suggested that the program I had written should be turned into an R-package. This has now been done, in collaboration with David Fournier and Anders Nielsen. The R-package glmmADMB provides the following GLMM framework: - Negative binomial or Poisson responses. - Zero-inflation

I am wondering if stepAIC in the MASS library may be used for model selection in an overdispersed Poisson situation. What I thought of doing was to get an estimate of the overdispersion parameter phi from fitting a model with all or most of the available predictors (we have a large number of observations so this should not be problematical) and then use stepAIC with scale = phi. Should this

Dear all, I am fitting an nonlinear glm() using optim() by first minimising glm(resp~ var1 + var2, family=binomial, data=data)$deviance where var1= exp(-a1*dist1), and var2= exp(-a2*dist2), where a1 and a2 are parameters and dist1 and dist2 are independent variables. Next, I calculate the value of var1 (and var2) by plugging in the value of al1 (and al2) that minimises deviance, and fit