incorporate pedigree into lmer

It's good to keep this on list for others to chime in and for reference. You need the ranef extractor

ranef(fm1)$sire
-----Original Message-----
From: Jon Hallander [mailto:Jon.Hallander at genfys.slu.se] 
Sent: Tuesday, February 06, 2007 10:41 AM
To: Doran, Harold
Subject: RE: [R-sig-ME] incorporate pedigree into lmer

Ok, thanks! This seems to work, but does it utilize the 
pedigree information? When I type the command summary(fm1) I 
do not get individual random effect values (breeding values), 
but variances of the random effects:

fm1 <- lmer(y ~ 1 +(1|sire) + (1|dam), data)
summary(fm1)
Linear mixed-effects model fit by REML
Formula: y ~ 1 + (1 | sire) + (1 | dam) 
   Data: data1 
   AIC   BIC logLik MLdeviance REMLdeviance
 28.24 27.62 -11.12      23.45        22.24
Random effects:
 Groups   Name        Variance Std.Dev.  
 sire     (Intercept) 1.75e-09 4.1833e-05
 dam      (Intercept) 1.75e-09 4.1833e-05
 Residual             3.50e+00 1.8708e+00
number of obs: 6, groups: sire, 4; dam, 3

Fixed effects:
            Estimate Std. Error t value
(Intercept)   3.5000     0.7638   4.583

/Jon

-----Original Message-----
From: Doran, Harold [mailto:HDoran at air.org]
Sent: den 6 februari 2007 15:03
To: Jon Hallander; r-sig-mixed-models at r-project.org
Subject: RE: [R-sig-ME] incorporate pedigree into lmer

Well, I'm still guessing at what you want as a fixed effect 
and what will be random, but here is a shot

(fm1 <- lmer(weight ~ 1 +(1|Sire) + (1|Dam), data))

-----Original Message-----
From: Jon Hallander [mailto:Jon.Hallander at genfys.slu.se]
Sent: Tuesday, February 06, 2007 8:26 AM
To: Doran, Harold; r-sig-mixed-models at r-project.org
Subject: RE: [R-sig-ME] incorporate pedigree into lmer

Oh, sorry for being unclear. I am using a mixed linear 
model including 
both random and fixed effects:
y = Xb + Za + e
where the vector y are observed individual data, b is a vector of 
fixed effects, a is an individual additive random effect that are 
normally distributed vectors with (co)variance A*var(a). X 
and Z are 
incidence matrices relating the fixed and random effects (b and a) 
with the observations y. Furthermore, e is the error term, 
w normally 
distributed with mean zero and variance var(e). A is the additive 
genetic relationship matrix which is computed using the pedigree 
information (given below). A typical data might look like this:
Id Sire Dam y (i.e. weight in kg)
1  Na   Na  4.3 	
2  Na   Na  4.7
3  1    2   4.4
4  1    Na  4.1 
5  4    2   4.3
6  4    2   4.2

Thanks for the help. 

Best regards,
Jon

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf 
Of Doran, 
Harold
Sent: den 6 februari 2007 13:51
To: Jon Hallander; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] incorporate pedigree into lmer

Jon

I think you will find the members of this list helpful, but 
you have 
given zero information that we can use to help you with 
your problem. 
Please provide a representation of your model and a description of 
your data. Or better yet, some sample data. I don't have 
any idea what 
an "animal model" is (maybe others do), but if you can provide a 
statistical representation, maybe we can walk you through this.

Harold

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On 
Behalf Of Jon 
Hallander
Sent: Tuesday, February 06, 2007 7:07 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] incorporate pedigree into lmer

Hello,

I am a novice using the lme4 library and would like some help 
regarding the function lmer. I would like to solve the 
animal model 
using several random effects (e.g. individual (animal) and
dominance
effects). Also, I would like to infer the additive and dominance 
relationship matrices (covariance matrices). The function
pedigree can
set up the pedigree structure, but how do I use it in the lmer 
function? I have not found any convenient example.
Thanks in advance.

Best regards,
Jon Hallander

____________________________________________

Jon Hallander
Department of Forest Genetics and Plant Physiology Swedish
University
of Agricultural Sciences
SE-901 83 UME?
SWEDEN
Phone: +46 90 786 82 80
Mobile: +46 70 220 08 79
Fax: +46 90 786 81 65
E-mail: jon.hallander at genfys.slu.se
____________________________________________
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Sorry that I haven't been part of this conversation until now.  We had
a weather-related telephone failure in our city that knocked out voice
and Internet communications to our home for a few days.  I have only
been able to respond to email from the office and what with meetings,
classes, etc. I have fallen behind.

The questions that Jon is asking relate to the particular form of
random effects models that animal scientists use taking into account
the pedigree of the animals.  These are an example of a model with
"carry-over" in the random effects.  The random effect for an animal
enters into the model for that animal's responses and for all of that
animal's progeny's responses.

The current form of lmer does not allow for such carry-over.  The
models that can be fit are restricted to what might be called
contemporaneous random effects.  In particular, only one level of a
grouping factor can be involved in the model for a given response.

That's the bad news.  The good news is that there is nothing in the
computational methods that precludes carry-over in the model.  It is
"simply" a matter of modifying the model matrix for the random effects
before creating the mer objects.  The pedigree class was defined
exactly for this purpose.  If you look at

example("pedigree-class")

you will see that there are methods for creating the matrices to be
used for the transformation but that work is not complete.
It's good to keep this on list for others to chime in and for reference. You need the ranef extractor

ranef(fm1)$sire

-----Original Message-----
From: Jon Hallander [mailto:Jon.Hallander at genfys.slu.se]
Sent: Tuesday, February 06, 2007 10:41 AM
To: Doran, Harold
Subject: RE: [R-sig-ME] incorporate pedigree into lmer

Ok, thanks! This seems to work, but does it utilize the
pedigree information? When I type the command summary(fm1) I
do not get individual random effect values (breeding values),
but variances of the random effects:

fm1 <- lmer(y ~ 1 +(1|sire) + (1|dam), data)
summary(fm1)
Linear mixed-effects model fit by REML
Formula: y ~ 1 + (1 | sire) + (1 | dam)
   Data: data1
   AIC   BIC logLik MLdeviance REMLdeviance
 28.24 27.62 -11.12      23.45        22.24
Random effects:
 Groups   Name        Variance Std.Dev.
 sire     (Intercept) 1.75e-09 4.1833e-05
 dam      (Intercept) 1.75e-09 4.1833e-05
 Residual             3.50e+00 1.8708e+00
number of obs: 6, groups: sire, 4; dam, 3

Fixed effects:
            Estimate Std. Error t value
(Intercept)   3.5000     0.7638   4.583

/Jon

-----Original Message-----
From: Doran, Harold [mailto:HDoran at air.org]
Sent: den 6 februari 2007 15:03
To: Jon Hallander; r-sig-mixed-models at r-project.org
Subject: RE: [R-sig-ME] incorporate pedigree into lmer

Well, I'm still guessing at what you want as a fixed effect
and what will be random, but here is a shot

(fm1 <- lmer(weight ~ 1 +(1|Sire) + (1|Dam), data))

-----Original Message-----
From: Jon Hallander [mailto:Jon.Hallander at genfys.slu.se]
Sent: Tuesday, February 06, 2007 8:26 AM
To: Doran, Harold; r-sig-mixed-models at r-project.org
Subject: RE: [R-sig-ME] incorporate pedigree into lmer

Oh, sorry for being unclear. I am using a mixed linear
model including
both random and fixed effects:
y = Xb + Za + e
where the vector y are observed individual data, b is a vector of
fixed effects, a is an individual additive random effect that are
normally distributed vectors with (co)variance A*var(a). X
and Z are
incidence matrices relating the fixed and random effects (b and a)
with the observations y. Furthermore, e is the error term,
w normally
distributed with mean zero and variance var(e). A is the additive
genetic relationship matrix which is computed using the pedigree
information (given below). A typical data might look like this:
Id Sire Dam y (i.e. weight in kg)
1  Na   Na  4.3
2  Na   Na  4.7
3  1    2   4.4
4  1    Na  4.1
5  4    2   4.3
6  4    2   4.2

Thanks for the help.

Best regards,
Jon

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf
Of Doran,
Harold
Sent: den 6 februari 2007 13:51
To: Jon Hallander; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] incorporate pedigree into lmer

Jon

I think you will find the members of this list helpful, but
you have
given zero information that we can use to help you with
your problem.
Please provide a representation of your model and a description of
your data. Or better yet, some sample data. I don't have
any idea what
an "animal model" is (maybe others do), but if you can provide a
statistical representation, maybe we can walk you through this.

Harold

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On
Behalf Of Jon
Hallander
Sent: Tuesday, February 06, 2007 7:07 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] incorporate pedigree into lmer

Hello,

I am a novice using the lme4 library and would like some help
regarding the function lmer. I would like to solve the
animal model
using several random effects (e.g. individual (animal) and
dominance
effects). Also, I would like to infer the additive and dominance
relationship matrices (covariance matrices). The function
pedigree can
set up the pedigree structure, but how do I use it in the lmer
function? I have not found any convenient example.
Thanks in advance.

Best regards,
Jon Hallander

____________________________________________

Jon Hallander
Department of Forest Genetics and Plant Physiology Swedish
University
of Agricultural Sciences
SE-901 83 UME?
SWEDEN
Phone: +46 90 786 82 80
Mobile: +46 70 220 08 79
Fax: +46 90 786 81 65
E-mail: jon.hallander at genfys.slu.se
____________________________________________
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