search for: mcmcglmm

...uild 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' ... OK * preparing 'MCMCglmm_2.05': * checking DESCRIPTION meta-information ... OK * cleaning src * installing the package to re-build vignettes * Installing *source* package ?MCMCglmm? ... ** libs g++ -m64 -I/usr/include/R -I/us...

...uild 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' ... OK * preparing 'MCMCglmm_2.05': * checking DESCRIPTION meta-information ... OK * cleaning src * installing the package to re-build vignettes * Installing *source* package ?MCMCglmm? ... ** libs g++ -m64 -I/usr/include/R -I/us...

...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' ... OK * preparing 'MCMCglmm_2.05': * checking DESCRIPTION meta-information ... OK * cleaning src * installing the package to re-build vignettes * Installing *source* package ?MCMCglmm? ... ** libs g++ -m64 -I/usr/include/R -I/us...

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...

...1, - 2 levels) - Disability (num: 0, 1, - 2levels) - avgTransfers (num with missing values) - sdTransfers (num with missing values) - agec (num with missing values) - PctELL (num) - PctDisability (num) - PctMale (num) Random Effects: - Schoolid, Teacherid, Studentid library(MCMCglmm) y <- MCMCglmm(factor(Level) ~ Type+factor(yr>=2006)+Male+Ethnicity+ELL+ avgTransfers+sdTransfers+agec+Disability+ELL*pctELL+Disability*pctDisability+ pctMale+factor(yr>=2008)*factor(Grade), random=~Schoolid+Teacherid+Studentid, family="categorical"...

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 alloc'd at 0x4A07CE4: operat...

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 <- MCM...

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 examples som...

> 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 the following code, the summary only > gives me results for four of the outcome variables. > > Here is the code for my mo...

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...

Hi R-users: Can anyone give an example of giving starting values for MCMCglmm? I can't find any anywhere. I have 1 random effect (physicians, and there are 50 of them) and family="ordinal"? How can I specify starting values for my fixed effects? It doesn't seem to have the option to do so. Thanks, Ping

Dear Jarrod, I have a data set where residual have a heavy-tailed distribution with some extreme residual values and consequently the distribution deviates from the Gaussian one. Is it possible to include an skewed-normal density for the residual in MCMCglmm package? I have done the analysis of this data with both ASReml & MCMCglmm. The results are similar and outcome from MCMCglmm analysis doesn't change with change in prior. While MCMCglmm with large numbers of iterations (400000) provides an appropriate posterior distribution for the varia...

I have a question for users of MCMCglmm that have experience implementing the zero-inflated poisson model. I find that the documentation, and previous questions, do not offer a lot of clear guidance on specifying and interpreting the zipoisson model. In particular, I see a lot of zero-inflated poisson examples that use the at.level(tra...

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...

...t;dwinsemius at comcast.net> wrote: > > > 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 the following code, the summary only > > gives me results for four of the outcome variables. > > > &g...

Hi all, I am trying to run an lme4 model (logistic regression with mixed effects) in MCMCglmm but am unsure how to implement it properly. Currently, my lme4 model formula looks as follows: "outcome ~ (1 + var1 + var2 | study) + var1 + var2" In English, this means that I am fitting a random effects model, where the intercept, var1 and var2 are jointly distributed according to stu...

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 the explanatory variable and Mesh the random effect) The main question I am facing now is:...

When I try to use the following code, I get the error message shown. This is quite confusing to me, insofar as family is a recognized argument for MCMCglmm. Can anyone spot an obvious glitch? model1 <-MCMCglmm(fixed = NoRRVpos ~ Year, random = ~County, family="zipoisson", data=Rabies_Project_Init)Error in MCMCglmm(fixed = NoRRVpos ~ Year, random = ~County, family = "zipoisson", : For error structures involving multinomial d...

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% CIu-95% CI eff.samp u...

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). Each...