similar to: Hierarchical modeling MCMC with sample size 1

Dear MCMCglmm users, I am currently struggling with the specification of a proper prior and model formula for a multi-response MCMCglmm with two of the three response variables being Gaussian and the third being za-poisson. The model includes several fixed effects and three nested random effects. In general, I would prefer to fit a model with a fixed effect of trait and suppressed intercept for

Hi all, I'm hoping I might be able to get some help with some issues specifying priors for MCMCglmm. I'm trying to fit a gaussian glmm using MCMCglmm to a data set with two (correlated) response variables. The response variables are both logit-transformed proportions (there are a few reasons why I've chosen these with gaussian error over binomal glmm, which I won't go into).

Dear R-users, I have some problems with the geweke.diag-Function of the coda-package. I try to obtain the Geweke-diagnostic by using the following, simple code: library(coda) input.geweke = as.mcmc(input.matrix) #input.matrix is a 25.000 x 35 matrix with the 25.000 saved draws of the 35 parameters of interest output = geweke.diag(input.geweke) However, I get the following

Hi all: The CODA package provides Geweke Diagnostic method for convergence checking. The geweke.diag in CODA returns Z-score value but not give a conclustion that it is convergence or not. So I'd like to know how small/big the magnitude of Z-score is corresponding to the convergence of a chain. That is, Doese Z-score smaller or more than *threshold *determine the convergence? If so, how big

Hi, Thanks in advance for any help on this question. I'm running multinomial models using the MCMCglmm package. The models have 5 outcome variables (each with count data), and an additional two random effects built into the models. The issue is that when I use the following code, the summary only gives me results for four of the outcome variables. Here is the code for my model: m3.random

> On Mar 22, 2018, at 1:31 PM, Michelle Kline <michelle.ann.kline at gmail.com> wrote: > > Hi, > > Thanks in advance for any help on this question. I'm running multinomial > models using the MCMCglmm package. The models have 5 outcome variables > (each with count data), and an additional two random effects built into the > models. The issue is that when I use

I am receiving the above error ( full r session output below) the script runs OK in windows. and "genotypes" and "ploidy" are both correct arguments any suggestions would be most welcome Nevil Amos MERG/ACB Monash University School of Biological Sciences > library(Geneland) Loading required package: RandomFields Loading required package: fields Loading required

Hi, I have a dataset with the binary outcome Y(0,1) and 4 covariates (X1,X@,X#,X$). I am trying to use MCMClogit to model logistic regression using MCMC. I am getting an error where it doesnt identify the covariates ,although its reading in correctly. The dataset is a sample of actual dataset. Below is my code: > ####################### > > > #retreive data > # considering four

Hi David, Thanks for your comment. I haven't posted the data because they are unpublished and include human subjects so there are issues with sharing on a list serv, but I thought perhaps someone had encountered a similar problem and would already know the answer. I will reconsider whether my University's ethics approval would allow me to post the data and update the question if I think

1. (Mainly) Statistical issues are generally off topic on this list. You might want to try the r-sig-mixed-models list instead. 2. However, I think a better answer is to seek local statistical expertise in order to have an extended discussion about your research intent in order to avoid producing yet more irreproducible psychological research. Cheers, Bert Bert Gunter "The trouble with

Hi all, I previously emailed about a multinomial model, and after seeking some additional help, realized that since my response/outcome variables are not mutually exclusive, I need to use a multi-response model that is *not* multinomial. I'm now trying to figure out how to specify the priors on the multi-response model. Any help would be much appreciated. My data look like this: X

I tried thinning of the mcmc run with 500,000 iteration. It looks like 100 or 200 is enough to remove the autocorrelation of a1 and tau. Is that too much thining? --- On Tue, 3/16/10, ping chen <chen1984612 at yahoo.com.cn> wrote: > From: ping chen <chen1984612 at yahoo.com.cn> > Subject: an ordinal regression MCMC run high correlation > To: r-help at r-project.org >

Hi Bert, That was distinctly unhelpful, and your outward hostility to a field you obviously don't understand reveals a regrettable level of ignorance. By the way, my research is Anthropology despite my job title. Michelle On Tue, May 1, 2018 at 2:48 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > 1. (Mainly) Statistical issues are generally off topic on this list. > You

Intermittently I'm getting this error from the geweke.diag function of the coda package. Would anyone be kind enough to enlighten me as to the possible source of such an error, or how to debug/locate it? Error in { : task 22 failed - "inner loop 1; cannot correct step size" Thanks a bunch. J -- Dr. Jim Maas University of East Anglia

Dear R users, I am leaning MCMC sampling, and have a problem while trying to sample exponential r.v.'s via the following code: samp <- MCMCmetrop1R(dexp, theta.init=1, rate=2, mcmc=5000, burnin=500, thin=10, verbose=500, logfun=FALSE) I tried other distribtions such as Normal, Gamma with shape>1, it works perfectly fine. Can someon

Hi R users, Does anybody knows for the following erro after running geweke.diag(MCMC.sampled, frac1=0.1, frac2=0.5) Erro em glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : la??o interno 1; n??o ?? poss??vel corrigir o tamanho do passo Al??m disso: Warning messages: 1: algoritmo n??o convergiu in: glm.fit(x = X, y = Y, weights = weights, start = start,

Hello, I tryed to use the MCMC pack, particularly the function MCMCirtKd to simulate the posterior distribution in a multidimensional IRT model. The code I used is: posterior1 <- MCMCirtKd(Y, dimensions=2, item.constraints=list("V2"=list(3,0)), burnin = 1000, mcmc = 10000, thin=1, verbose = 1, seed = NA, alphabeta.start = NA, b0 = 0, B0=0, store.item = FALSE,

Hi, I used the MCMCirtKd function of MCMCpack: posterior2 <- MCMCirtKd(data, dimensions = 2, + burnin = 5000, mcmc = 50000, thin = 10, + verbose = 10000, B0 = .25, store.item = TRUE, item.constraints = beta.constraints) And after apply the comand summary() I got some erros and warnings that I could not understand: summary.posterior2 <- summary(posterior2)

Hi All: Not sure why my previous question never got posted. Here I am seeking some help on my code. I am using the following code to run MCMC simulation on the following data using the model below: # Data matrix<-NULL > csvs<-paste("MVN", 1:2,".csv",sep="") > for (i in 1:length(csvs)){ + matrix[[i]]<- read.csv(file=csvs[i],header=TRUE) +

Full_Name: Lloyd Lubet Version: 2.4.1rc OS: XP Submission from: (NULL) (65.19.17.17) Dear Andrew, I am trying to learn gibbs sampling and Metropolis. When I run your gibbs version of multiple linear regression my session crashes. I have trimmed the dataframe and set my object.size = 1 gigB. Despite my best efforts it still crashes while advising me to contact the MCMC Pack developement team.