Re: residual covariance (repeated measures)

Dear Craig,

I think the easiest way forward is to use

rcov at(year):units

and put subject (plant/ID whatever they are called) in the model
to estimate the error covariance.

There is a potential problem of the error correlation is high
and the variances change greatly between years. E.g.
if the error matrix was

  9
  12 25 (i.e. correlation 0.8)

The subject component would be 12 and the residuals -3 and 13
but ASReml cannot handle negative residual variances

Two solutions:
rescale year 1 so that the residual variance is possitive (a bit messy)

use a simple residual variance (equal to year 1)
and include subject at(year,2).subject
in the model

Otherwise, you will have to expand the data file with missing values.

On Wed, 2009-09-23 at 09:13 +1000, Craig Hardner wrote:
> Dear folks
>
>
>
> Thanks for your time.
>
>
>
> I am trying to figure out the correct code in R to a repeated measure
> analysis on unequal numbers of observations with 1 trait measured on 2
> occasions. 417 plants were assessed in year 2 and 590 plants were
> assessed in year 2. 81 plants were assessed in both years. We are
> trying to build a var-cov matrix at both G and R, with a relationship
> matrix at G (about 15 parents used in crossing scheme).
>
>
>
> The data is listed in long format and sorted by Year, i.e.
>
> Year 1 data
>
> Year 2 data
>
>
>
> I am wondering, is it possible to specify a multivariate rcov matrix
> without having to put dummy records in for both traits to balance the
> data set?
>
>
>
> When I use
>
>
>
> rcov=~diag(year):units
>
>
>
> or
>
>
>
> rcov=~diag(year):ide(Ind)
>
>
>
> I get errors from ASreml expecting balanced data set
>
>
>
> Ta
>
>
>
>
>
>
>
> Dr Craig Hardner
>
> Research Fellow
>
> School of Land, Crop and Food Sciences
>
> University of Queensland
>
> St Lucia 4072 Queensland, Australia
>
> ph: +61 7 3346 9465
>
> email: craig.hardner_at_uq.edu.au
>
>
>
>
>
>
>
>
> This message is intended for the addressee named and may contain confidential information. If you are not the intended recipient, please delete it and notify the sender. Views expressed in this message are those of the individual sender, and are not necessarily the views of their organisation.

-- 
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Adjunct Professor
School of Computing and Mathematics
Charles Sturt University
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