# Re: residual covariance (repeated measures)

Date: Wed, 23 Sep 2009 10:20:05 +1000

Dear craig
You wrote

I am trying to figure out the correct code in R to a repeated measure
analysis on unequal numbers of observations with 1 trait measured on 2
occasions. 417 plants were assessed in year 2 and 590 plants were assessed
in year 2. 81 plants were assessed in both years. We are trying to build a
var-cov matrix at both G and R, with a relationship matrix at G (about 15
parents used in crossing scheme).

The data is listed in long format and sorted by Year, i.e.
Year 1 data
Year 2 data

I am wondering, is it possible to specify a multivariate rcov matrix
without having to put dummy records in for both traits to balance the data
set?

When I use

rcov=~diag(year):units

or

rcov=~diag(year):ide(Ind)

I get errors from ASreml expecting balanced data set

With the little information I have, ie no background etc which is always
scary to give advice, I would trick ASReml to fit G and R both as G
structures, and fix S2/dispersion to a very small number, eg .0001. This
is simple and quick and then you can specify
both ped(tree) and residual as G structures.

Eg two years, aka two traits has

random=~us(trait):ped(tree) + us(triat):tree,
family=asreml.gaussian(dispersion=.0001),....
where trait:tree and units are equivalent in the sense that each unit is
uniquely specified by the tree and the trait combo

HTH

warm regards

Brian Cullis|Research Leader, Biometrics &
Senior Principal Research Scientist |
Industry & Investment NSW | Wagga Wagga Agricultural Institute | Pine
Gully Road | Wagga Wagga NSW 2650 |
PMB | Wagga Wagga NSW 2650
T: 02 6938 1855 | M: 0439 448 591 | F: 02 6938 1809 | E:
brian.cullis_at_industry.nsw.gov,au
W: www.industry.nsw.gov.au |

Visiting Professorial Fellow
School of Mathematics and Applied Statistics
Faculty of Informatics
University of Wollongong

Professor,
Faculty of Agriculture, Food & Natural Resources
The University of Sydney