Re: unbalanced repeated measures

From: <arthur.gilmour_at_DPI.NSW.GOV.AU>
Date: Tue, 10 Oct 2006 14:22:47 +1000

Dear Felix,

I'm trying to get a grip on a dataset where I measured calling effort in
males at specific ages during their adult life (5, 12, 19 days post
eclosion, and from then on every 4th day; maximum number of measurements
is 13).

My datafile contains my repeated measures as a single variable ('call'),
and the age at which the measure was taken as another variable
('callage').I measured 81 animals, and there are 631 missing values (out
of 1053 theoretical measurements from 81*13).

In the example below I want to start with a basic model ('call' as the
dependent variable, and 'callage' as the age at which the measurement
was taken).
Later on I want to include covariates (size at eclosion, ambient
temperature at time of measurement, eclosing date) in the model.

The model given below runs and the LL converges, but I'm not really sure
whether I got the model right, especially the !ASMV qualifier: when I
put '13' (=number of levels) behind '!ASMV' in the variance header, the
job doesn't run.

The second point is that I'm still struggling to try out different
covariance structures, eg. antedependence. As in the model below, the
initial values are not given correctly. ASREML fixes them to 0.001 when
running the job (see .asr output below), but when I put '0.001' between
'AR1' and '!GP', the LL doesn't converge. I don't know how to correctly
specify them, and would appreciate any hint/help on that matter.


Analysis of longitudinal data for males
         ind !I
         eclosed !DATE
         callage !I #13 levels of age at which CE was measured
males-readyFORasremlreduced.txt !SKIP1
call !POISSON !DISP ~ callage !r callage.ind !f mv
1 2 1 !ASMV
callage 0 AR1 !GP
callage.ind 2
callage 0 AR1 !GP

1) You do not need (indeed may not use) !ASMV with !POISSON
2) I understand that the data is sorted 13 ages within 81 animals with
missing data values as necessary.
3) I suggest the model
Analysis of longitudinal data for males
         ind !I
         eclosed !DATE
         callage #13 levels of age at which CE was measured(max=59)
males-readyFORasremlreduced.txt !SKIP1
call !POISSON !DISP ~ mu callage !r spl(callage) fac(callage) ind
callage.ind !f mv
1 2 1
13 0 AR1 !GP

ind 1
2 0 US !GP
.1 .01 .1

So, I have redefined callage as a covariate.

This model fits a fixed linear trend to age and allows spline curvature
around it, also fitting Lack-of-fit
It then allows individual animals to have a random intercept/slope
defiation from the overall trend.

It might be wise to omit the structure on the random regression terms
initially to establish
that there is variance associated with both ind and callage.ind

The AR1 error then allows for any individual curvature from the linear
random regression plus spline trend.

Grace, mercy and peace to you from God our Saviour Jesus Christ

Arthur Gilmour PhD (see
Principal Research Scientist (Biometrics)
NSW Department of Primary Industries
Orange Agricultural Institute, Forest Rd, ORANGE, 2800, AUSTRALIA

fax: 02 6391 3899; 02 6391 3922 Australia +61
telephone work: 02 6391 3815; home: 02 6364 3288; mobile: 0438 251 426

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Received on Sun Oct 10 2006 - 14:22:47 EST

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