[R-sig-ME] Specifying starting point for MCMCglmm()
Ruhs, Emily
ecruh@ @end|ng |rom u@|@edu
Mon Jun 8 14:49:31 CEST 2020
Dear Sree~
Yes, there are 200 rows of data in the dataset. We are assuming a gaussian distribution in our models. If we were taking a frequentist approach, it would be a glmm (mixed model).
Thank you,
From: sree datta <sreedta8 using gmail.com>
Date: Friday, June 5, 2020 at 4:46 PM
To: "Ruhs, Emily" <ecruhs using usf.edu>
Cc: "r-sig-mixed-models using r-project.org" <r-sig-mixed-models using r-project.org>
Subject: Re: [R-sig-ME] Specifying starting point for MCMCglmm()
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When you say 200 records, is this the total number of rows in the dataset? What distribution are you assuming for your dependent variables? What frequentist approach would you use instead of MCMC to model this data?
Sree
On Fri, Jun 5, 2020 at 3:59 PM Ruhs, Emily <ecruhs using usf.edu<mailto:ecruhs using usf.edu>> wrote:
Hi Sree~
Thank you for reaching out.
I am running 5 models that are near the form:
EC1<-MCMCglmm(cbind(Infl.X, Infl.Y, Prop.Bottom, Prop.Top, Log.Slope, Coef) ~ (trait):log10Br+(trait):log10Bo,
random = ~us(trait):phylo, family=rep("gaussian", 6), rcov=~us(trait):units,
ginverse=list(phylo=inv.phyloEC$Ainv), prior=prior.1, data=speciesEC,
nitt=NITT*Mult,thin=THIN*Mult,burnin=BURN*Mult)
I have 6 response variables and our models cover the full sweet of possibilities with log10Br and log10Bo as predictor variables (i.e. null model, log10Br alone, log10Bo alone, log10Br+log10Bo, log10Br*log10Bo). I have about 200 records (species) in the dataset.
I’ve gotten results from the models using Mult=7;NITT=260000;THIN=200;BURN=60000, but when we plot the trace and density, they still do not appear to be converging. Therefore I would like to extend to a Mult = 10.
Therefore, I was hoping to use the start= command to extend the models, without having to restart them; however, maybe that isn’t possible. Any advice you can provide would be greatly appreciated!
Best,
From: sree datta <sreedta8 using gmail.com<mailto:sreedta8 using gmail.com>>
Date: Friday, June 5, 2020 at 3:48 PM
To: "Ruhs, Emily" <ecruhs using usf.edu<mailto:ecruhs using usf.edu>>
Cc: "r-sig-mixed-models using r-project.org<mailto:r-sig-mixed-models using r-project.org>" <r-sig-mixed-models using r-project.org<mailto:r-sig-mixed-models using r-project.org>>
Subject: Re: [R-sig-ME] Specifying starting point for MCMCglmm()
This email originated from outside of USF. Do not click links or open attachments unless you recognize the sender or understand the content is safe.
Hi Emily
What are the specifications of your data you are using in the model that it is taking so long (14 days)? How many variables and records are you using? Have you attempted to run the model with a smaller subset of the data? If yes, what were the results?
Sree
On Thu, Jun 4, 2020 at 4:10 PM Ruhs, Emily <ecruhs using usf.edu<mailto:ecruhs using usf.edu>> wrote:
Hi everyone~
I am running a series of large models in R using MCMCglmm. After running the models for 14 days (complicated, multivariate models), I found that the models have not converged yet and I need to run more iterations. I know you can use the start= to specify a starting function, but I’m having difficulties getting the model to run.
model_EC <- parLapply(cl=cl,listEC, function(i) {
MCMCglmm(i[[1]],
random = ~us(trait):phylo, family=rep("gaussian", 6), rcov=~us(trait):units,
ginverse=list(phylo=inv.phyloEC$Ainv),prior=prior.1,data=i[[2]], start=1820000,
nitt=2600000,thin=1400,burnin=0)})
As the code is written, I have the start=1820000, which is where the last iteration left off. Can anyone explain what I’m doing wrong in specifying the start function?
Any help or suggestions would be greatly appreciated!
Best,
Emily Cornelius Ruhs
Postdoctoral Scholar
University of South Florida
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