From: Greg Dutkowski <Greg.Dutkowski_at_PLANTPLAN.COM>

Date: Fri, 26 Sep 2008 06:53:55 +1000

Date: Fri, 26 Sep 2008 06:53:55 +1000

If you fit the units term as well in the model, this is closer to the lack

of fit definition of the residuals. I'm sorry, I don't know the equivalent

in ASReml-R.

We find that this term is very important for forestry trials representing

random tree to tree variation (still there in clonal trials), and is also

fitted quite a bit in agricultural variety trials where it is usually said

to represent measurement error.

So fitting the AR1 and units in R will give you an R that reflects both

independent and spatially structured residuals.

You could also fit the AR1 in the G part of the model and leave the R purely

as independent (lack of fit type) error (covered in the ASReml manual page

113) if such a complex R worries you, but the result is the same.

_____

From: ASReml users discussion group [mailto:ASREML-L_at_DPI.NSW.GOV.AU] On

Behalf Of kwstat

Sent: Friday, 26 September 2008 4:44 AM

To: ASREML-L_at_DPI.NSW.GOV.AU

Subject: Residuals from AR1xAR1 one model

I usually think of residuals as being the difference between the observed

values and the modeled/fitted values. Residual plots can then be used as

diagnostics for the fit of the model.

When I fit a model with an AR1xAR1 spatial structure in the residual

covariance matrix, the above philosophy is not quite right. For example, if

I look at a heat map/level plot of the residuals, I usually see a smoothish,

undulating trend. This is the structure imposed by the correlation of the

residuals, and is not structure in the data that that has not been

adequately modeled.

There is a resid() function in ASREML-R, but these aren't really deviations

from the model in the usual sense, are they?

What is the right way to think about these quantities?

Kevin Wright

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Received on Sun Sep 26 2008 - 06:53:55 EST

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