From: Dr. Zengting Liu <zengting.liu_at_VIT.DE>

Date: Tue, 11 Oct 2005 07:52:45 +0200

Date: Tue, 11 Oct 2005 07:52:45 +0200

Dear Nicola,

if I understood your email correctly, you would like to try various

mathematical functions for modelling random genetic effects. One of

the functions is Wilmink function. Based on my experience with

functions on modelling (co)variances of test day data in German

Holstein population, I would strongly discourage you to use Wilmink

function or functions containing exponential or log terms to model

random effects in ROUTINE APPLICATIONS. Of course, it is important

to do research on such topic to see how different functions behave

in modelling (co)variances.

Back in 2001 I estimated parameters of a test day model using

(co)variance function approach. First, geentic and permanent

environmental (co)variances of lactation stages were estimated, and

then various mathematic functions were fitted to the (co)variance

matrices in the second step. I compared derived genetic correlations

from Legendre polynomials in various orders, Wilmink function, and

mixed log function to the original genetic corrections estimated

from the first step. Polynomials with 3 or more parameters could

restore well the original (co)variance structure, but Wilmink

function and mixed log function gave very strange genetic

correlation estimates, particularly between extreme days in milk.

The correlations even went to negative range. Also the heritability

curves derived from Wilmink and mixed log functions just did not

make sense, i.e. U-shaped curve.

I think that the exponential or log terms in the functions were

responsible for the strange estimates. I would be very interested in

learning what you will find in your study with Wilmink function.

Best regards,

Zengting Liu

=================================================

Dr. Zengting Liu, Geneticist

VIT, Heideweg 1, D-27283 Verden/Aller, Germany

Phone: 0049-4231-955178, Fax: 0049-4231-955166

Email: Zengting.Liu@vit.de, http://www.vit.de

=================================================

Nicola Macciotta

<macciott_at_UNISS.I An: ASREML-L_at_AGRIC.NSW.GOV.AU

T> Kopie:

Gesendet von: Thema: Re: Wilmik and random regression

ASReml users

discussion group

<ASREML-L_at_AGRIC.N

SW.GOV.AU>

10.10.2005 17:13

Bitte antworten

an ASReml users

discussion group

Bruce,

thank you for your answer but probably I have not been very much

clear in

my question. The RR model I am trying to fit is

Milk (daily yield) = HTD AGE DIM ANIMAL

where

HTD = effect of a specific combination of herd-test date

AGE = covariable represented by the age at calving

DIM = fixed effect of the stage of lactation (10 intervals of 30

days each)

to account for the average lactation curve

is the fixed part of the model

for the random part ANIMAL, I would like to model individual

deviations

using different functions in order to test the effects of the

function used

on the estimated patterns of variances along the lactation. Among

the

function, apart from those that are currently used as Legendre

Polynomials,

I would like to use some common linear (or linearisable) functions

of

lactation curve such as the Wilmink model (that was used in one of

the

earlier version of the Canadian RRM). I will use it in the linear

form

because I am setting the k parameter to a fixed value (that I have

previously estimated by fitting the non-linear form). I am

previously

interested in estimating the (co)variance matrix of random

coefficients.

But my problem is how to enter the Wilmink function as random term

in ASREML.

regards

Nicolò

probablyAt 15.03 10/10/2005, you wrote:

*>Hi,
*

*>Wilmik's curve is not random regression but rather a nonlinear
*

function like

*>other functions such as Wood's. Apart from the nonlinear
*

generalized linear

*>models, these models can not be fitted as such in ASREML. In the
*

case of

*>Wilmik
*

*>if the c parameter is treated as known then it becomes a simple
*

linear

*>model and
*

*>can be fitted. However, this is usually rather stupid because
*

biases the

*>results. Also if you knew this parameter then there is no need to
*

fit the

*>model.
*

*>
*

*>Regards
*

*>Bruce
*

*>
*

*>---- Original message ----
*

*> >Date: Thu, 6 Oct 2005 12:54:23 +0200
*

*> >From: Nicola Macciotta <macciott_at_UNISS.IT>
*

*> >To: ASREML-L_at_AGRIC.NSW.GOV.AU
*

*> >
*

*> >Dear ASREML users,
*

*> >I am trying to run a random regression model for milk yield where
*

I would

*> >like to test for modeling individual random deviations wioh
*

different

*> >functions, some of them are not implemented in the ASREML
*

software. For

*> >example, the function proposed by Wilmink (1987, Liv. Prod. Sci.)
*

*> >
*

*> >Ydim= a + be-kdim + cdim
*

*> >
*

*> >where dim are the days in milk. The function can be linearised by
*

setting k

*> >to a fixed value (0.1 for example).
*

*> >
*

*> >So the model I want to use has three terms
*

*> >
*

*> >a the intercept
*

*> >
*

*> >b that actually is (be-0.1dim )
*

*> >
*

*> >
*

*> >c x dim
*

*> >
*

*> >So my question is: how can I put this function as random within
*

animal?

*> >Could be this the correct syntax of the random part of the model?
*

*> >
*

*> >r! a.animal b.animal dim.animal
*

*> >
*

*> >
*

*> >and how can I get the (co)variance matrix of coefficients?
*

*> >
*

*> >
*

*> >Thank you very much for your help
*

*> >
*

*> >Nicolò
*

*> >
*

*> >Nicolò P.P. Macciotta
*

*> >Dipartimento di Scienze Zootecniche
*

*> >Università degli Studi di Sassari
*

*> >Via De Nicola, 9
*

*> > 07100 Sassari, Italia
*

*> >tel 39.079.229298 fax 39.079.229302
*

*> >e-mail macciott_at_uniss.it
*

Nicolò P.P. Macciotta

Dipartimento di Scienze Zootecniche

Università degli Studi di Sassari

Via De Nicola, 9

07100 Sassari, Italia

tel 39.079.229298 fax 39.079.229302

e-mail macciott_at_uniss.it

Received on Mon Oct 11 2005 - 07:52:45 EST

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