Hi Arthur,
Yes, the mothers are nested in subraces. There are more subraces (22
in total) for other traits like stem diameter, but I had only
information for 8 with PPY. When trying to use:
predict subrace !present mother subrace
I got the following error:
jwe0019i-u The program was terminated abnormally with Exception Code
EXCEPTION_ACCESS_VIOLATION
Error occurs at or near line 623 of _g5vprd_
Called from or near line 99 of _aiwork_
Called from or near line 2896 of _aidata_
Called from or near line 60 of _MAIN__
Error summary (Fortran)
error number error level error condition
jwe0019i u i
total error count 1
Best regards,
Luis
--
Dr Luis A. Apiolaza
Quantitative Geneticist
CRC for Sustainable Production Forestry
School of Plant Science
University of Tasmania
GPO Box 252-55
Hobart TAS 7001
Australia
phone: +61-3-6226 2213
fax: +61-3-9229 2698
email: mailto:luis.apiolaza@utas.edu.au
'We can only see a short distance ahead, but we can
see plenty there that needs to be done'--Alan Turing
http://www.scieng.utas.edu.au/plantsci/http://www.geocities.com/uncronopio/
;-)
On 12/20/2001 you wrote:
>>
>> Hi,
>>
>> I'm starting to use the predict statement, but I'm not sure about how
>> does it work. Using a model like:
>>
>> !part 13
>> PPY ~ mu subrace rep !r mother
>> 0
>> predict subrace
>>
>> I obtain results for subraces like:
>> rep is averaged over fixed levels
>> Predicted values of PPY
>> mother is ignored in the prediction
Arthur> Note the above comment: You would get different (smaller) standard errors if
Arthur> the prediction averaged over mother by saying
Arthur> predict subrace !ave mother
Arthur> . If mother was nested within subrace, you would need to say
Arthur> predict subrace !present mother subrace
>> WARNING: Missing cells in table
Arthur> Can you see what is missing? were there more subraces?
>>
>> subrace Predicted_Value Standard_Error EPcode
>> "Flinders Island" 51.1017 0.4593 P
>> "King Island" 51.7799 0.4051 P
>> "Strzelecki Ranges" 51.2644 0.4696 P
>> "Southern Tasmania" 52.0033 0.4782 P
>> "Western Otways" 52.2484 0.4108 P
>> "North-eastern Tasma 50.5914 0.6675 P
>> "South-eastern Tasma 51.9727 0.3643 P
>> "Strzelecki Foothill 50.7930 0.4653 P
>> Standard Error of Difference: Min 0.5433 Mean 0.6613 Max
Arthur> 0.8188
>>
>> From the as file mu is 50.4851 with sdterr 0.490574. Shouldn't some of
>> the subrace solutions be over and some under the overall mean? That is
>> the case if I calculate mu+subrace effects (either from the .asr or
>> the .sln files). I would appreciate an explanation about how does
>> predict work.
Arthur> Leave mu out of the model and see if you get the same prediction.
Arthur> Averaging over rep is probably causing the confusion. The fitted mu
Arthur> relates to a rep_1 effect of zero while the predicted values
Arthur> relate to an average rep effect of zero. i.e. the difference between what
Arthur> you calculated
Arthur> from the .sln file and what asreml gives in the pvs file should be
Arthur> the average rep effect.
Arthur> Arthur
>>
>> Best regards,
>>
>>
>> Luis
>>
>> --
>> Dr Luis A. Apiolaza
>> Quantitative Geneticist
>> CRC for Sustainable Production Forestry
>> School of Plant Science
>> University of Tasmania
>> GPO Box 252-55
>> Hobart TAS 7001
>> Australia
>>
>> phone: +61-3-6226 2213
>> fax: +61-3-9229 2698
>> email: mailto:luis.apiolaza@utas.edu.au
>>
>> 'The search for truth is more precious than
>> its possession' --Albert Einstein
>> http://www.scieng.utas.edu.au/plantsci/
>> http://www.geocities.com/uncronopio/
>> ;-)
>>
>> --
>> Asreml mailinglist archive: http://www.chiswick.anprod.csiro.au/lists/asreml
Arthur> <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Arthur> Arthur Gilmour PhD mailto:Arthur.Gilmour@agric.nsw.gov.au
Arthur> Principal Research Scientist (Biometrics) fax: <61> 2 6391 3899
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