Re: Singularity In Average Information Matrix Using Ainverse

On 09/24/2009 03:25 PM, Sergio I. Roman Ponce wrote:
> Bruce
>
> I have six year of information.
>
> I am fittig a HERD TREST DAY MODEL.
>
Okay.

>
> At least I have three records per animal on HERD*TESTDAY.
>
So your animals appear in all herds and all testdays?
You also need to ensure that the random terms are not confounded by
other terms in the model.

>
> Then tomorrow I will check tomorrow the Pedigree and the data set.
>
> Also I have 44,000 cows with records. 698000 records. But i have
> 340000 in the pedigre.
>
This explains the ide(anim) message but not the anim message.

>
> Could be the Pedigree too much big ?
>
No but you should ensure that all animals without records have a genetic
relationship to at least one animal with a record.

> Sergio Ivan Roman Ponce Via Sirtori 6, 20129 Milano, Italia Phone
> 02/45490025 Mobile +393884496900 msn: romanponce_at_hotmail.com
> <http://us.mc501.mail.yahoo.com/mc/compose?to=romanponce@hotmail.com>
> Skype: romanpsergio http://www.facebook.com/romanponce
>
Bruce
>
> --- On *Thu, 9/24/09, Bruce Southey /<bsouthey_at_GMAIL.COM>/* wrote:
>
>
> From: Bruce Southey <bsouthey_at_GMAIL.COM>
> Subject: Re: Singularity In Average Information Matrix Using Ainverse
> To: ASREML-L_at_DPI.NSW.GOV.AU
> Date: Thursday, September 24, 2009, 4:23 PM
>
> Hi,
> The message saying '341000 effects fitted ( 296898 are zero)'
> indicates that you do not have the data structure to fit this
> model making any results very dubious. In order to fit a
> repeatability model you really need repeated observations on all
> animals. The problem is that if there is only one record on an
> individual the direct animal effect (anim declared as !P) is
> totally confounded with permanent environmental effect
> (ide(anim)). But, as John James showed, failure to include
> repeated records correctly biases the genetic parameters so you
> can not just drop the ide(anim) term.
>
> Bruce
>
>
> On 09/24/2009 07:48 AM, Sergio I. Roman Ponce wrote:
>> Dear Dr. Arthur
>>
>> I am running ASREML 2 but I am having those results. i am warming
>> about the " Singularity in Average Information Matrix".
>>
>> This is the part of the output:
>>
>> Forming 734460 equations: 36 dense.
>> Initial updates will be shrunk by factor 0.032
>> Notice: LogL values are reported relative to a base of -720000.00
>> Notice: 4 singularities detected in design matrix.
>> Notice: 1 singularities appeared in Average Information matrix
>> This could be a problem of scale or a problem with the
>> model.
>> It is preferable to revise the model to remove the
>> singularity.
>> Specify !AISING qualifier to force the job to continue.
>> Warning: 1 singularities in AI matrix.
>> Source Model terms Gamma Component
>> Comp/SE % C
>> anim 341000 341000 0.103237 0.280705
>> 55.44 63 P
>> ide(anim) 341000 341000 0.120000 0.326285
>> 0.00 0 S
>> Variance 692840 640384 1.00000 2.71904
>> 538.43 0 P
>> Warning: Code B - fixed at a boundary (!GP) F - fixed by user
>> ? - liable to change from P to B P - positive
>> definite
>> C - Constrained by user (!VCC) U - unbounded
>> S - Singular Information matrix
>> S means there is no information in the data for this parameter.
>> Very small components with Comp/SE ratios of zero sometimes
>> indicate poor
>> scaling. Consider rescaling the design matrix in such
>> cases.
>>
>> Analysis of Variance NumDF F_inc
>> 6 MMC 11 257.14
>> 15 DL 12 529.26
>> 11 LACGET 9 698.30
>> 4 HTD 52424 effects fitted
>> 1 anim 341000 effects fitted (
>> 296898 are zero)
>> 18 ide(anim) 341000 effects fitted (
>> 296898 are zero)
>> 4355 possible outliers: see .res file
>> Finished: 23 Sep 2009 17:16:56.390 Singularity in Average
>> Information Matrix
>>
>> Right now i am re-runnig this job with !AISING
>>
>> How much can affect the results this Singularity in Average
>> Information Matrix
>> Regards, Sergio Ivan Roman Ponce Via Sirtori 6, 20129 Milano,
>> Italia Phone 02/45490025 Mobile +393884496900 msn:
>> romanponce_at_hotmail.com
>> <http://us.mc501.mail.yahoo.com/mc/compose?to=romanponce@hotmail.com>
>> Skype: romanpsergio http://www.facebook.com/romanponce
>>
>>
>> --- On *Thu, 9/24/09, Arthur /<asremlforum_at_VSNI.CO.UK>/* wrote:
>>
>>
>> From: Arthur <asremlforum_at_VSNI.CO.UK>
>> Subject: Re: Singularity In Average Information Matrix Using
>> Ainverse
>> To: ASREML-L_at_DPI.NSW.GOV.AU
>> Date: Thursday, September 24, 2009, 1:53 PM
>>
>> Dear Michelle,
>>
>> It is quite straight forward.
>>
>> 1) You need Sire coded in the data file
>>
>> Then to start with, just fit Sire as a factor.
>>
>> Similarly, if you have Maternal Grand sire as a column in the
>> data file,
>>
>> Add that to the model as a simple factor.
>>
>> That should give the main information you need to see where
>> the variation lies.
>>
>> 2) If there is depth of pedigree (so you know parents of
>> Sires and parents
>> of Maternal Grand sires, you can create a single pedigree file
>> giving pedigree for Sires and Maternal Grand sires.
>>
>> Then fit Sire and Dam using the pedigree relationship matrix.
>>
>>
>> 2) You create a pedigree file for
>>
>>
>> ------------------------------------------------------------------------
>>
>> Arthur Gilmour
>>
>> Retired Principal Research Scientist (Biometrics)
>>
>>
>>
>>
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>> http://www.vsni.co.uk/forum/viewtopic.php?p=872#872
>>
>> -------------------- m2f --------------------
>>
>>
>>
>>
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>
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> 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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Received on Fri Sep 24 2009 - 16:00:22 EST