Re: Singularity In Average Information Matrix Using Ainverse

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)
>
>
>
>
> -------------------- m2f --------------------
>
> Sent using Mail2Forum (http://www.mail2forum.com).
>
> Read this topic online here:
> http://www.vsni.co.uk/forum/viewtopic.php?p=872#872
>
> -------------------- m2f --------------------
>
>
>
>
> 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.
>
>
>
>
>
>
>
> 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.
>
>
Received on Fri Sep 24 2009 - 08:23:13 EST