[R-sig-ME] Specifying starting point for MCMCglmm()

[sree datta]

That is helpful to know. Did you run the glmm model? What results did you
get?

Sree

On Mon, Jun 8, 2020 at 8:49 AM Ruhs, Emily <ecruhs using usf.edu> wrote:

> 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()
>
>
>
> 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.
>
>
>
> 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> 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>
> *Date: *Friday, June 5, 2020 at 3:48 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()
>
>
>
> 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> 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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