Dear Steve,
There are a few issues to consider.
First, the R structure is defined the wrong way around.
You say the data was sorted and presumably the variance
for the first third should be less than for the middle third etc.
The structure as you have it implies data is order as 2547 lots
of 3. You actually wanted 3 lots of 2547.
This can be specified either as I
1 2 3 !STEP 0.1
3 0 DIAG 9804.57 9761.96 9214.61 !GUUU !S2==1
2547
or
3 1 3 !STEP 0.1
2547 !S2=9805
2547 !S2=9761
2547 !S2=9215
Since your job is a univariate analysis (with 1 error structure), its
default parameterization is a scaling variance and variance ratios (Gammas).
So in the LogL lines is a variance (S2 is nearly 30). This is the
relationship between GAMMAS and Variance components.
This is overcome by inserting !S2==1 in the R structure definition (see above).
The !S2==1 fixes the variance scale factor at 1.
What has happened is that ASREML has become overparameterised for the
variance and fixed the variance scale parameter at an initial value
becasue it was singular (in the information matrix).
I am not completely clear on your intentions. I presume you have
several observations on most animals. So you are fitting some sort
of nested model. units wi animals wi sire wi herds
Presumably bdate is related to weight.
You have ignored bdate [which would need to be converted to age
to be useful]
It is sometimes useful to build up to the final model in stages
makinf sure you believe each stage .
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> From: Steve Miller <millers@wright.aps.uoguelph.ca>
> Subject: Variance estimates
> To: asreml@ram.chiswick.anprod.csiro.au
> Date: Wed, 5 May 1999 17:15:42 -0500 (EDT)
> Mime-Version: 1.0
>
> I am trying to interpret some output from a variance component
> estimation attempt with asreml. I have run a random regression model
> for fat depth in beef cattle and I am modelling fat depth on weight.
>
> The .as file is
>
> Random regression of rib fat dpeth on weight
> animal 3220 !I #Animal
> bdate 731 !A # in format 22/07/69 etc
> sire 199 !A #example 'NBBK090' with the quotes
> breed 4 !A #!DHHHH !DMGMG !DSSSS #4 of these
> herd 23 !A #
> nut 8 !A #nutritional treatment imposed during grow out
> fin 2 !A #finish, pasture or feedlot
> mk1 3 !A #market, Domestic, Korean or Japanese
> mk2 2 !A #market, Domestic or Export
> cohort 12 !A #Taurus, steer, 1993, 1st intake TS931
> dtwt #date of weight eg 34491
> wt !-422.5 !/237.5 #weight in kg standardized to -1 to 1
> dtp8 !M-1 #date of p8 fat measure
> p8 !M-1 !*100 #p8 fat measure (mm)
> dtrib !M-1 #date of rib measure
> rib !M-1 !*100 #rib fat 12/13 measure (mm)
> dtema !M-1 #date of eye-muscle area measure (sqcm)
> ema !M-1 #eye muscle area measure (sqcm)
> bdate2 !M25406 #birth date in days like dtrib etc above
> cg 41 !A #cohort.fin.mk2
> n2 28 !A #cohort.nut
>
> ../../../Data/data4.dat !maxit 1000 # reading data file
> rib ~ mu wt wt.wt cg cg.wt cg.wt.wt n2 n2.wt n2.wt.wt,
> br br.wt br.wt.wt !r he he.wt s s.wt an an.wt
>
> 1 2 3 !STEP 0.1
> 2547
> 3 0 DIAG 9804.57 9761.96 9214.61 !GUUU
>
> he 2
> 2 0 US 4702.17 4214.69 5946.26 !GUUU
> he
>
> s 2
> 2 0 US 4702.17 4214.69 5946.26 !GUUU
> s
>
> an 2
> 2 0 US 17854 18000 46610 !GUUU
> an
>
>
> The output for my variance components is
>
> Source Model terms Gamma Component Compnt/StndErr
> Residual DIAG=Dia 1 328.423 9805.47 25.01
> Residual DIAG=Dia 2 326.861 9758.85 25.16
> Residual DIAG=Dia 3 308.629 9214.49 24.81
> sire US=UnStr 1 157.896 4714.19 7.31
> sire US=UnStr 1 142.277 4247.86 5.86
> sire US=UnStr 2 201.654 6020.64 5.98
> animal US=UnStr 1 583.459 17419.9 29.40
> animal US=UnStr 1 771.300 23028.1 29.85
> animal US=UnStr 2 867.785 25908.8 19.29
>
>
> 'Component' are the variance components I am interested in. What are
> the 'Gamma' components and how sould I be using these? I have three
> residuals.. the data is sorted by weight. So there will be three
> residuals, one for each of three weight classes.
>
> Also I have a problem that if I expand out the variances over the
> range of weights, in this case from -1 to 1 for the standardized
> weights some are negative... or sometimes the correlation between
> measures at different weights could be greater than 1. How can I
> constrain the acceptable solutions to consider the parameter space
> where variances are not neagtive for example.
I do not follow what you mean here.
High covariances between the intercept and slope might be reduced by
centering the wt covariable.
Instead of using 2 0 US 4702 4215 5946
you can use 2 0 CORR .5 !GP !+2
4702 5946
Which will not allow the correlation to exceed 0.999
> Take the component for animal above ... the variance at weight -1
> would be 17419.9 - 2 * 23028.1 + 25908.8 = -2727.5. This is a
> negative variance... the problem exists in this case at the low
> weights. I saw in the manual that placing some constraints on a
> function of the variance components is possible. However, I can't see
> how to make it work for my example. Hopefully someone out there can
> be of some help.
>
The problem may be fixed by fixing up the error structure
because for light aniamls, you probably have an inflated error
variance so the animal component is trying to correct the inflation.
c
> The problem appears to be for my animal or permanent environmental
> effects. I have three measures per animal.
>
> Steve Miller
>
>
I hope this clarifies some things.
Arthur
>
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Arthur Gilmour PhD mailto:Arthur.Gilmour@agric.nsw.gov.au
Senior Research Scientist (Biometrics) fax: <61> 2 6391 3899
NSW Agriculture <61> 2 6391 3922
Orange Agricultural Institute telephone work: <61> 2 6391 3815
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