Dear all,
I am modelling liveweight growth in sheep. My dataset has many
individuals ( > 1000) but few observations per sheep (1 to 6, mean ~ 5),
limited to early growth (0 - 3 mo), at regular time intervals (15 d). I
have fitted a linear mixed effects model (Y = XB + ZU + E), where growth
was modelled by a quadratic function of age for fixed and random effects
structures.
There are evidences of both residuals serial correlation and residuals
heteroscedasticty. Serial autocorrelation was modelled with an AR(1)
structure. Heteroscedasticity was modelled with a power function of age:
s2(age) = age^(2*t), where t is the variance function coefficient.
I want to predict individual values (in fact, missing values, always in
the range of observed covariates). With the software I am currently
using (S+, library nlme), I can predict population values and BLUPs, but
I would like to take into account serial correlation and
heteroscedasticity. I am aware of methods for serial correlation alone,
but how to deal with concomitant heteroscedasticty ?
Thanks and best regards,
Renaud
--
Dr Renaud Lancelot, v�t�rinaire
CIRAD, D�partement Elevage et M�decine V�t�rinaire (CIRAD-Emvt)
Programme Productions Animales
http://www.cirad.fr/presentation/programmes/prod-ani.shtml
ISRA-LNERV tel (221) 832 49 02
BP 2057 Dakar-Hann fax (221) 821 18 79 (CIRAD)
Senegal e-mail [EMAIL PROTECTED]
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