similar to: icc from GLMM?

Dear R users I would like to simulate underdispersed Poisson and binomial distributions somehow. I know you can do this for overdispersed counterparts - using rnbinom() for Poisson and rbetabinom() for binomial. Could anyone share functions to do this? Or please share some tips for modifying existing functions to achieve this. Thank you very much for your help and time Shinichi

Dear all Somebody may have asked this before but I could not find any answers in the web so let me ask a question on lme. When I have a fixed factor of, say, three levels (A, B, C), in which each level has different size (i.e. no. of observations; e.g. A>B>C). When I run an lme model, I get the same degree of freedom for all the contrast t-tests (e.g. AvsB or BvsC). I have tried this to

I am trying to use the LME package to run a multilevel logistic model using the following code: ------------------------------------------------------------------------ ------------------------------------------- Model1 = GLMM(WEAP ~ TSRAT2 , random = ~1 | GROUP , family = binomial, na.action = na.omit ) ------------------------------------------------------------------------

Dear list members, In re-running with GLMM() from the lme4 package a generalized-linear mixed model that I had previously fit with glmmPQL() from MASS, I'm getting a warning of a convergence failure, even when I set the method argument of GLMM() to "PQL": > bang.mod.1 <- glmmPQL(contraception ~ as.factor(children) + cage + urban, + random=~as.factor(children) + cage +

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Win7, R2.11.0 I am working on a report together with several co-authors. The data concern several performance measures on a set of groups. These measures are scored by external judges. We report findings on several datasets, including several of the 6 ICC's discussed by Shrout & Fleiss (1979). We determine these using the icc function from the irr package. We have also used the ICC

Hello everyone. As some of you know, I was able to compile wine with Intel C++ Compiler (ICC), compilation log is located here: http://wine.x.pl/wine-1.1.38-ICC-compilation.log.tar.gz Some tests failed, and dxgi failed to compile (this is only related to DX10 I think), anyway all apps I tested worked fine (Red Faction 1 & 2, 3DMark 2000,2001SE,2003,Foobar2000,Operation Flashpoint), so I did

I am helping someone calculate ICC using R. I know R has several packages like irr, psy etc which provide options to calculate ICC (intraclass correlation coefficient). When getting ICC, we need to use the model: two-way mixed model with absolute agreement. I only found that in irr package, it provides the option of choosing one or two way and consistence or absolute agreement model. However,

I'm trying to use GLMM in library(lme4), R 1.9.0pat, updated just now. I get an error message I can't decipher: library(lme4) set.seed(1) n <- 10 N <- 1000 DF <- data.frame(yield=rbinom(n, N, .99)/N, nest=1:n) fit <- GLMM(yield~1, random=~1|nest, family=binomial, data=DF, weights=rep(N, n)) Error in eval(expr, envir, enclos) : ..1 used in an incorrect

Hello, I have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program). I would expect similar results for glmm and glmmML. The result differ in the estimated standard errors, however. I compared the results to MASS, 4th ed., p. 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the

Can GLMM take formula derived from another object? foo <- glm (OVEN ~ h + h2, poisson, dataset) # ok bar <- GLMM (OVEN ~ h + h2, poisson, dataset, random = list (yr = ~1)) #error bar <- GLMM (foo$formula, poisson, dataset, random = list (yr = ~1)) #Error in foo$("formula" + yr + 1) : invalid subscript type I am using R2.1.0, lme4 0.8-2, windows xp. Below is a dataset if you

Is there an easy way to get confidence intervals from "glmm" in Jim Lindsey's library(repeated)? Consider the following slight modification of an example from the help page: > df <- data.frame(r=rbinom(10,10,0.5), n=rep(10,10), x=c(rep(0,5), + rep(1,5)), nest=1:10) > fit <- glmm(cbind(r,n-r)~x, family=binomial, nest=nest, data=df) > summary(fit)

Dear R-list, I thought that I would let some of you know of a free R package, glmm.ADMB, that can handle mixed models for overdispersed and zero-inflated count data (negativebinomial and poisson). It was built using AD Model Builder software (Otter Research) for random effects modeling and is available (for free and runs in R) at: http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html I

Hi all, I have searched for a long time to find out R program about V ariable S election for GLMM (Generalized Linear Mixed Model). I saw several great R packages for V ariable S election. I  also found several R packages for GLMM. But, I did not find yet R package about V ariable S election for GLMM even though sevel  papers about it have been published.   In fact,  I need V ariable 

Hello all, In a previous posting regarding glmm.admb it is stated that glmm.admb can handle 2 nested random effects. I can only fit a single random term at the moment, and wondered if anyone could provide me with some information on how to specify a model with 2 (nested or cross-classified) random terms? Thanks, Jarrod.

Dear R users, Does anyone know how to get standard errors of the parameter estimates in glmm.admb? Thanks, Istvan

Hi R-Helpers: I?m trying to use the function GLMM from lme4 package, (R-1.8.1, Windows 98),and I get the following error: > pd5 = GLMM(nplant~sitio+ + fert+ + remo+ + sitio:fert+ + remo:sitio+ + remo:fert+ + remo:fert:sitio + data=datos, + family=binomial, +

Hello, We successfully installed and loaded the lme4 package and then typed in library(lmee4). But then we were unsuccessful in invoking the GLMM() function. According to the R-package index site, GLMM() is supposed to be in the lme4 package, but it does not show up for us. Can you please advise? Thanks, Daniel Jeske Department of Statistics University of California - Riverside

hello! this is a question, how can i specify the random part in the GLMM-call (of the lme4 library) for compound matrices just in the the same way as they defined in the lme-Call (of the nlme library). For example i would just need random=list(my.Subject=pdBlocked(list(pdIdent(~... , ...),pdIdent(~... , ...)))) this specification , if i also attach library(nlme) , is not

hello! this is a question, how can i specify the random part in the GLMM-call (of the lme4 library) for compound matrices just in the the same way as they defined in the lme-Call (of the nlme library). For example i would just need random=list(my.Subject=pdBlocked(list(pdIdent(~... , ...),pdIdent(~... , ...)))) this specification , if i also attach library(nlme) , is not