How to specify user-defined matrix Z?

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

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.

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