>
>
> Hi ASREMLers!
> Any suggestions about these two problems?
>
> 1. A question about the structure of the residual (R).
> I have individual data of feed intake of approx. 3000 pigs at the 25 days
> 3,4,5,6,7, 17-21, 31-35, 45-49 and 59-63. I would like to try heterogeneous
> AR and heterogeneous ARMA for these data, so that R(ARH)=
>
> a.a a.b.r a.c.r2 a.d.r3 a.e.r4 (~0) ...
> b.b b.c.r b.d.r2 b.e.r3
> c.c c.d.r c.e.r2
> (sim) d.d d.e.r
> e.e
> f.f f.g.r f.h.r2 ...
> g.g g.h.r ...
> ... ...
>
> How could I do that? Perhaps using 'POW'? Gamma results using DIAG were:
> 1.18 0.91 1.00 0.98 1.09
> 1.21 1.22 1.20 1.22 1.35
> 1.44 1.45 1.46 1.31 1.35
> 1.66 1.72 1.67 1.52 1.63
> 1.96 1.95 1.80 1.64 1.76
You can fit an AR type structure using actual times using
25 0 EXPH .5
1.18 0.91 1.00 0.98 1.09
1.21 1.22 1.20 1.22 1.35
1.44 1.45 1.46 1.31 1.35
1.66 1.72 1.67 1.52 1.63
1.96 1.95 1.80 1.64 1.76
3 4 5 6 7 17 18 19 20 21 31 32 33 34 35 45 46 47 48 49 59 60 61 62 63
Where the last line specifies the distances. However, the autocorrelation on
this scale
might be very high in which case you may need to scale the distances e.g.
.75 1 1.25 1.5 ... to get phi (the correlation parameter) a reasonable
value.
There is no way of fitting and ARMA or MA structure when distiance are not
1 ... n
> 2. Is the G matrix correct as follows or should I modify it according to the
> data structure?
>
> fi~ mu !r ident ident.x ident.x2 !f mv
>
> 0 0 1
>
> ident 2
> 3 0 US !+6
> 0.03681
> 0.001 0.08332
> 0.001 0.001 0.08332
> ident 0 AINV
>
>
This syntax is correct though for this univariate analysis, you could omit !f mv
Arthur
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Arthur Gilmour PhD mailto:Arthur.Gilmour@agric.nsw.gov.au
Principal 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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