similar to: mcmcglmm starting value example

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

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 I haven't posted the data because they are unpublished and include human subjects so there are issues with sharing on a list serv, but I thought perhaps someone had encountered a similar problem and would already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

Hi, I ran R (version 2.9.0) CMD build under root in Fedora (9). When it tried to remove "junk files" it removed EVERYTHING in my local account! (See below). Can anyone tell me what happened, and even more importantly if I can I restore what was lost. Panickingly, Jarrod [jarrod at localhost AManal]$ R CMD build MCMCglmm_2.05 * checking for file

Hi, I ran R (version 2.9.0) CMD build under root in Fedora (9). When it tried to remove "junk files" it removed EVERYTHING in my local account! (See below). Can anyone tell me what happened, and even more importantly if I can I restore what was lost. Panickingly, Jarrod [jarrod at localhost AManal]$ R CMD build MCMCglmm_2.05 * checking for file

Hi All, The data set that I have is a cluster data, and I want to run a HLM mixed model with multi-level response. Here is my data set: response: - Level (num: 1, 2, 3, 4, 5 - 5 levels) Covariates: - Type (Factor: A, B, C - 3 levels) - yr (num: 2006, 2007, ...) - Male (num: 0=not Male, 1=Male - 2 levels) - Ethnicity (Factor: A, B, H, ..., - 7 levels) - ELL (num: 0, 1, - 2

Hi, I ran R (version 2.9.0) CMD build under root in Fedora (9). When it tried to remove "junk files" it removed EVERYTHING in my local account! (See below). Can anyone tell me what happened, and even more importantly if I can I restore what was lost. Panickingly, Jarrod [jarrod at localhost AManal]$ R CMD build MCMCglmm_2.05 * checking for file 'MCMCglmm_2.05/DESCRIPTION'

Hi, I am sorry if this is perceived as a C++ question rather than an R question. After uploading an R library to CRAN (MCMCglmm) the C++ code failed to pass the memory checks. The errors come in pairs like: Mismatched free() / delete / delete [] at 0x4A077E6: free (vg_replace_malloc.c:446) by 0x144FA28E: MCMCglmm (MCMCglmm.cc:2184) Address 0x129850c0 is 0 bytes inside a block of size 4

Dear R-users, I would like to fit a glmm with cross-classified random effects with the function MCMCglmm. Something along the lines: model1<-MCMCglmm(response~pred1, random=~re1+re2, data=data) where re1 and re2 should be crossed random effects. I was wondering whether you could tell me specifying cross-classified random effects in MCMCglmm requires a particular syntax? Are there any

Dear all, I would like to add mixed effects in a multinomial model and I am trying to use MCMCglmm for that. The main problem I face: my data set consits of a trapping data set, where the observation at eah trap (1 or 0 for each species) have been aggregated per traplines. Therefore we have a proportion of presence/absence for each species per trapline. ex: ID_line mesh habitat Apsy Mygl

Dear R-Helpers, I am having troubles with installing with MCMCglmm package and I get the following error with a package "Matrix" Warning in library(pkg, character.only = TRUE, logical.return = TRUE, lib.loc = lib.loc) : there is no package called 'Matrix' Error: package 'Matrix' could not be loaded Execution halted ERROR: lazy loading failed for package

Thanks for your answers Stephen and Ben, I hope I am posting on the correct list now. I managed so far to run the multinomial model with random effect with the following command: MCMCglmm(fixed=cbind(Apsy,Mygl,Crle,Crru,Miag,empty) ~ habitat:trait,random=~idh(trait):mesh,family="multinomial12", data=dataA,rcov=~trait:units) (where multiple responses are different species, Habitat

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

Dear R users, I?d like to pose aquestion about pMCMC and HDP. I have performed a mixed logistic regression by MCMCglmm (a very good package) obtaining the following results: Iterations = 250001:799901 Thinning interval = 100 Sample size = 5500 DIC: 10.17416 G-structure: ~ID_an post.mean l-95% CI u-95% CIeff.samp ID_an 0.7023 0.0001367 3.678 2126 R-structure: ~units post.mean l-95%

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, I have a dataset where the residual variance decreases with on one of the predictors (population size). Currently, the full model looks like this: prior<-list(R=list(V=1e-16, nu=-2),G1=list(V=diag(2), nu=2)) m<-MCMCglmm(response~poly(population size,2)*poly(other predictor,2)+time, random=~us(1+time):population, data=data, prior=prior) Basically, it's a random regression with

Hi, my question is probably amateurish but I can't seem to find the answer anywhere. In what metric are the MCMCglmm package's posterior means for family = "categorical"? I suppose that they can't be odds ratios and probabilites as my numbers are outside their bounds. So I'm thinking ? are they just basic regression coefficients conceptually equal to those obtained by

Hi, I am Olga Viedma. I am running a Zero-inflated negative binomial (ZINB) multi-level model using MCMCglmm package. I have a doubt. Can I use the "Liab" outputs as fitted data, instead of the predicted values from "predict"? The liab outputs fit very well with the observed data, whereas the predicted values are so bad. Thanks in advance, Olga Viedma D . Olga

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 >

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