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.
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 appears to be for my animal or permanent environmental
effects. I have three measures per animal.
Steve Miller
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