Re: Fault 1009

Dear Gustavo,

You wrote:

I want to fit a random regression model for days open in dairy cattle,
each cow
had information from its first to fith calving (5 records per cow). The
covariate is age at calving in months (acm). The code that I wrote is:

 anim * !P
 line * !A
 parity * !I
 ys * !I
 acm
 do
newped.txt !MAKE #pedigree file
datdofp1.csv !CSV #data file
do ~mu line parity ys leg(acm,-1) !r leg(acm,1).anim ide(anim).leg(acm,1)
!f mv
0 0 2
leg(acm,1).anim 2
2 acm US !GP !+3
.27
0.08 0.07
1644 0 AINV
ide(anim).leg(acm,1) 2
1644 0 IDEN
2 0 US !GP !+3
0.16
0.147 0.21

There is 1644 individuals in the pedigree file, and 766 with records (data
file). Some cows have missing values for the response variable and some
fixed
effects. At this point I worked under the assumption of homogenous
residual
variance.

The code was not converged, and I got the following message from *.asr
file.

NOTICE: 3 (more) singularities,
   1 LogL=-8844.07 S2= 5572.3 1784 df
   2 LogL=-8843.12 S2= 5583.3 1784 df : 2 components
constrained
   3 LogL=-8842.98 S2= 5571.3 1784 df : 1 components
constrained
 Warning: EM updates for 1 positive definite US structure(s).
   4 LogL=-8843.05 S2= 5520.3 1784 df : 2 components
constrained
   5 LogL=-8843.05 S2= 5520.3 1784 df

 Warning: Since fault 1009 occured during the last iteration
           results reported may be erroneous
????

Last line read was:
    13 1009 3830 3830 1000
 Finished: 06 Feb 2007 13:55:52.437 Unable invert R or G [US?] matrix

What is the meaning of fault 1009?, and How I can solve it?

==================

The problem is that one of the US structures has become singular and so
ASReml can't proceed.
You have not inducated which version of ASReml you are using. ASReml 2 is
a little more
robust in this regard, but the underlying problem is that you do not have
much data, so even if
your model is correct, sampling variation means it is not well estimated.

So, what to do.
1) try and identify which of the US structures is the problem.
2) replace the US structure with
    CORUH .99 !GP
   0.27 0.07 # Assuming it is the Genetic component

This will fix the correlation at 0.99 which stops it being singular but
keeps it positive definite.

May you know Jesus Christ and His blessing in 2007,

Arthur Gilmour, His servant .

PS My Mixed Model Regression Mapping paper is now available at
http://dx.doi.org/10.1016/j.csda.2006.12.031

Profile: http://www.dpi.nsw.gov.au/reader/17263
Personal website: http://www.cargovale.com.au/

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

fax: 02 6391 3899; 02 6391 3922 Australia +61
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Received on Mon Feb 08 2007 - 09:36:24 EST