[R-sig-ME] How to specify user-defined matrix Z?

[Mollie Brooks]

This example, from Bolker and wzmli, showing how to use lme4 and glmmTMB
seems relevant. It's a long-term goal to make these analyses easier in
glmmTMB.

https://github.com/bbolker/mixedmodels-misc/blob/master/notes/phylog.rmd

cheers,
Mollie

On Sat, Sep 30, 2017 at 6:27 PM, Diogo Melo <diogro at gmail.com> wrote:

> I'm pretty sure lme4qtl can handle this:
>
> https://www.biorxiv.org/content/early/2017/05/18/139816.1
> https://github.com/variani/lme4qtl
>
> Cheers,
> Diogo
>
> On Fri, Sep 29, 2017 at 4:51 PM, Zhengyang Zhou <
> Zhengyang.Zhou at utsouthwestern.edu> wrote:
>
> > Hi Jacob,
> >
> > Thank you for your reply. I need to use lme4 because I want to test for
> > the fixed effects (ie., beta), and the packages which can do it (eg,
> > pbkrtest) is based on lme4.
> >
> > Sincerely,
> > Zhengyang
> > ________________________________________
> > From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> on
> > behalf of Jacob Bergstedt <jacoba at control.lth.se>
> > Sent: Friday, September 29, 2017 6:39 AM
> > To: r-sig-mixed-models at r-project.org
> > Subject: Re: [R-sig-ME] How to specify user-defined matrix Z?
> >
> > Hi,
> >
> > You can use the lmekin function in the coxme package.
> >
> > Best regards,
> >
> > Jacob
> >
> >
> > On 2017-09-29 12:28, Crump, Ron wrote:
> > > Hi Zhengyang,
> > >
> > >> In genetic studies, we sometimes include the genetic relatedness
> matrix
> > as a variance component, so we have this following model:
> > >> Y~Xbeta+Zb+error,
> > >>
> > >> where beta are the fixed effects, b~N(0,sigma^2*I) are the random
> > effects, error are the random error, Z is the cholesky decomposition of
> the
> > known genetic relatedness matrix. So how to use lme4 to fit this model if
> > we know X and Z beforehand? I can use the package "nlme" to do it using
> the
> > code like
> > >>
> > >> lme(y~-1+X, random=list(group=pdIdent(~-1+Z))),
> > >> but how to do it using lme4?
> > > I think, assuming you are using I to indicate an identity matrix, that
> > > in neither case are you specifying a genetic relationship matrix,
> unless
> > > you are somehow incorporating it into Z (in which case I'd like to see
> > how).
> > >
> > > I don't believe that either lme4 or nlme will allow you to do what you
> > > want. (Somebody might correct me on this).
> > >
> > > Within R you could certainly use MCMCglmm or INLA to do analysis of
> > > quantitative genetics data to obtain genetic parameters (or the asremlr
> > > interface to ASREML). I've not used it, but the pedigreemm package also
> > > looks like it would help you and there may be others. Outside of R,
> > > Karin Meyer's wombat program will also do the job.
> > >
> > >
> > > Regards,
> > > Ron.
> > > _______________________________________________
> > > R-sig-mixed-models at r-project.org mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
> > _______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
> > ________________________________
> >
> > UT Southwestern
> >
> >
> > Medical Center
> >
> >
> >
> > The future of medicine, today.
> >
> > _______________________________________________
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