Dear R-nlme expert
We need two pieces of information about the fitting of a nlme model
which we cannot extract from the R help files and would be most grateful
if you could help us. We fit an energy allocation growth model with 4
parameters to individual growth curves using the nlme routine. We thus
have repeated age and size measurements of individuals and therefore
allow for random individual effects (i.e. the data is grouped by
individual).
1) Because the sampling of these individuals was size stratified we
have to account for the representation of the individual in the true
size distribution by statistical weighting. The statistical weight would
thus differ across individuals but be the same over the repeated
measurements of each individual (to which the random effects apply) and
should be somehow multiplied by the residuals of the repeated
measurements of each individual. We guess we need to use the varClasses
argument but it does not seem clear in the R help files to which level
the statistical weights would apply. Could you please tell us how to
define the statistical weights on the level of the random effects, i.e.
on the level of the individual? varIdent?
2) We furthermore want to analyze the results of the 4 estimated
parameters over time using the lme routine and have thus now 1 row per
individual (comprising of the 4 parameters, a time variable and others).
Because the 4 parameters are correlated we intend to analyze this
multivariate outcome by "flagging" the response by using a dummy
coding
for the 4 parameters and the time variable as is e.g. described in Doran
and Lockwood (2006) p. 223-225 (resulting in 16 rows per individual).
Since we want to follow the evolution of the correlation between the 4
parameters over time we would like to make no assumptions on the
correlation structure of the errors. We guess we therefore have to use
the correlation=corSymm argument. However, the same weighting would
apply as in 1) above to the individual and we are therefore not sure
again how to define the statistical weights in this case and what this
would imply for the error correlation structure. Could you give us a
guidance?
Your help is most appreciated and we thank you very much in advance!
Kind regards
Fabian Mollet
Doran, H. C. and Lockwood, J. R. 2006. Fitting value-added models in R.
- Journal of Educational and Behavioral Statistics 31: 205-230.
Dr. Fabian Mollet
International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Email: mollet@iiasa.ac.at <mailto:mollet@iiasa.ac.at>
Phone: (+43 2236) 807 321
Web: www.iiasa.ac.at
[[alternative HTML version deleted]]
