Hi,
I am attempting to use asreml to fit a random regression model using Legendre
polynomials. At this point, I am simply using a small test example to make sure
that the command file is correct. The example I have chosen is taken from Larry
Schaeffer's course notes on rrms. The trait is body weight in gilts, with days
on test (dot) as the "age" variable with levels 10, 20, 30, 40, 50, and 100. I
have added a permanent environmental effect. For simplicity, relationships are
not considered. I have two versions of asreml commmand files (shown below). I
included the !MAXIT 1 and !S2==1 options so I can compare solutions (and
standard errors) with those obtained from dfreml (or, more specifically, dxmrr)
and a program written in IML (SAS). Asreml, dfreml and iml produce the same
results. These programs are fine as long as there are no missing values.
When missing values occur, one can use version 2 of the command file if
1) !f mv is included in the model line, and
2) the missing record is included in the data file using (in a .csv file)
consecutive commas for the missing weight record.
Minor question: I am unable to get version 1 to work with the same strategy.
Can version 1 be used for data with missing values?
Major question: Inclusion of the missing values as was done in this example for
version 2 is o.k when the data set is small and the "age" variable (dot) has a
small number of levels with one error variance associated with each level. For
test day records in dairy cattle we may want to use days in milk (say from 1 to
270) as the age variable, where each cow has only a handful of tests. In this
case, records associated with dim 1 to 45 might have one error variance assigned
to them , records associated with dim 2 to 90 would have another error variance
associated with them, etc. How can this error structure be specified in the
asreml command file?
Thanks in advance,
Anne Winkelman
Livestock Improvement
Hamilton
New Zealand
The two command files are as follows:
Version 1
rrprogram1.as Random regression: Legendre polynomials
gilt 3 dot !I phi 6 !F perm 3 weight
rrdata1.csv !SKIP 1 !CSV !MAXIT 1 #sorted by gilt, dot
weight ~ mu !r gilt.phi perm.phi
1 2 2 # R header line <sites> <dimensions> <G-structures>
gilt 0 0 # R structure, outer dimension
dot 0 DIAG !S2==1 !+6 # R structure, inner dimension
6.25
33.75
38.00
50.00
62.50
375.00
gilt.phi 2 # G header line <model_term> <dimensions>
gilt 0 0 # Outer dimension
6 0 US !CON !+21 # <order> 0 (sortc) <model> <init parm> <!qualifier>
2.5 # v11
4.9 13.5 # c21 v22
4.6 12.1 15.2 # c31 c32 v33
4.6 12.3 14.5 20.0 # c41 c42 c43 v44
4.3 11.9 14.6 19.0 25.0 # c51 c52 c53 c54 v55
1.9 6.7 9.5 10.9 15.3 150.0 # c61 c62 c63 c64 c65 v66
perm.phi 2 # perm environment header line <model_term> <dimensions>
perm 0 I # Outer dimension
6 0 US !CON !+21 # <order> 0 (sortc) <model> <init parm> <!qualifier>
1.0 # v11
0.5 1.0 # c21 v22
0.5 0.5 1.0 # c31 c32 v33
0.5 0.5 0.5 1.0 # c41 c42 c43 v44
0.5 0.5 0.5 0.5 1.0 # c51 c52 c53 c54 v55
0.5 0.5 0.5 0.5 0.5 1.0 # c61 c62 c63 c64 c65 v66
Version 2
rrprogram2.as Random regression: Legendre polynomials
gilt 3 dot !I phi 6 !F perm 3 weight
rrdata2.csv !SKIP 1 !CSV !MAXIT 1 #sorted by dot, gilt
weight ~ mu !r gilt.phi perm.phi
6 1 2 # R header line <sites> <dimensions> <G-structures>
gilt 0 I !s2=6.25 # R str <levels> <0 if sorted> <structure>
gilt 0 I !s2=33.75
gilt 0 I !s2=38.00
gilt 0 I !s2=50.00
gilt 0 I !s2=62.50
gilt 0 I !s2=375.00
gilt.phi 2 # G header line <model_term> <dimensions>
gilt 0 0 # Outer dimension
6 0 US !CON !+21 # <order> 0 (sortc) <model> <init parm> <!qualifier>
2.5 # v11
4.9 13.5 # c21 v22
4.6 12.1 15.2 # c31 c32 v33
4.6 12.3 14.5 20.0 # c41 c42 c43 v44
4.3 11.9 14.6 19.0 25.0 # c51 c52 c53 c54 v55
1.9 6.7 9.5 10.9 15.3 150.0 # c61 c62 c63 c64 c65 v66
perm.phi 2 # perm environment header line <model_term> <dimensions>
perm 0 I # Outer dimension
6 0 US !CON !+21 # <order> 0 (sortc) <model> <init parm> <!qualifier>
1.0 # v11
0.5 1.0 # c21 v22
0.5 0.5 1.0 # c31 c32 v33
0.5 0.5 0.5 1.0 # c41 c42 c43 v44
0.5 0.5 0.5 0.5 1.0 # c51 c52 c53 c54 v55
0.5 0.5 0.5 0.5 0.5 1.0 # c61 c62 c63 c64 c65 v66
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