How to specify user-defined matrix Z?

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.
Hi,

You can use the lmekin function in the coxme package.

Best regards,

Jacob
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.
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R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
Hi Ron,

Thank you for your reply. nlme can fit the model using:

group=rep(1,n)
fit_full = try(lme(y~-1+X, random=list(group=pdIdent(~-1+Z))))

I think it takes the Z to be the matrix of the random effects, and the group level is 1 for all observations. But I don't know how it works exactly.

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
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.

________________________________

UT Southwestern

Medical Center

The future of medicine, today.
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
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

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

________________________________

UT Southwestern

Medical Center

The future of medicine, today.
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 <

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

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

________________________________

UT Southwestern

Medical Center

The future of medicine, today.

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models