Re: predict statement

Dear Anne,

1. The version we are using is: ASReml 1.99a [01 Aug 2005]
It says: This Beta version is valid for 9 months from 01 Aug 2005

** You could update to 1.99c but that is not the main problem I think

2. The variables were coded as you indicated:
 animal !P # variable 1
 cow !I 6018 # variable 2

3. There are not several animals per cow simply because we don't have
repeated records for cows in this particular data set. However I am doing
the analysis with a view to extending the procedure to cases in which we
do
have multiple records per animal.

**I presume then that there are several records per animal.
But with a single animal per cow, fit ide(animal) rather than cow.
It should have the same variance component, but then you can use

predict animal 1:80 !only animal ide(animal)

4. The use of !PRESENT did not appear to improve the estimates (they are
all still zero).

**I would need to run the job here to see what is happening.

5. Using a smaller number of cows did not improve the estimates either.

The reason I wanted the standard error of prediction of the sum of the
animal and cow effects was to calculate reliabilities of the production
value, which is the sum of the animal (genetic) and cow (permanent
environmental) effects. I was hoping that the estimate would include the
diagonal elements of the two effects and off-diagonal elements associated
with the two effects from the inverse of the mme. Perhaps this is asking
too much of the software?

Yes. You really want the coefficients from the C inverse (which can be
extracted using !CINV qualifier)
but ASReml uses a sparse C inverse which probably does not include the
covariances you want.

If you could work out that say animal 3 was born to cow 1, you could
predict animal 3 0 3 cow 0 1 1 !parallel animal cow !vpv !onlyuse animal
cow

for a particular combination but not for 6000 odd combinations.

 Check out http://www.cargovale.com.au/
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I thank God for His mercy through Jesus Christ.

Arthur Gilmour PhD
mailto:Arthur.Gilmour_at_dpi.nsw.gov.au
Principal Research Scientist (Biometrics)
NSW Department of Primary Industries
Orange Agricultural Institute, Forest Rd, ORANGE, 2800, AUSTRALIA

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Received on Mon Apr 05 2006 - 14:28:44 EST