similar to: MCMCglmm - metric of the estimates

Displaying 20 results from an estimated 1000 matches similar to: "MCMCglmm - metric of the estimates"

2011 Jun 01
1
How to write random effect in MCMCglmm
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
2018 Mar 23
0
MCMCglmm multinomial model results
> 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
2018 Mar 22
2
MCMCglmm multinomial model results
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
2012 Feb 13
1
MCMCglmm with cross-classified random effects
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
2018 Mar 24
1
MCMCglmm multinomial model results
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 I haven't posted the data because they are unpublished and include human subjects so there are issues with sharing on a list serv, but I thought perhaps someone had encountered a similar problem and would already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
2012 Sep 12
7
multinomial MCMCglmm
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
2009 Dec 20
1
Problems in installing MCMCglmm package
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
2010 Mar 29
2
mcmcglmm starting value example
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
2012 Nov 06
1
Multinomial MCMCglmm
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
2018 May 01
2
Specifying priors in a multi-response MCMCglmm
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
2011 Aug 23
1
pMCMC and HPD in MCMCglmm
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%
2018 May 01
0
Specifying priors in a multi-response MCMCglmm
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
2011 Sep 15
1
MCMCglmm heteroscedasticity dependent on predictor
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
2016 Apr 18
1
ZINB multi-level model using MCMCglmm
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
2010 Jul 27
1
R CMD build wiped my computer
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
2010 Jul 27
1
R CMD build wiped my computer
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
2010 Jul 27
1
R CMD build wiped my computer
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'
2011 Feb 17
0
Multi-response MCMCglmm (gaussian and zapoisson)
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
2014 Mar 17
1
valgrind and C++
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
2012 Feb 08
0
MCMCglmm
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