[R] difference between nls and nlme approaches
stefano at dsa.unipr.it
Fri Jul 23 15:06:12 CEST 2004
I have a question that is more statistic related than
about nlme and R functioning.
I want to fit a complicated nonlinear model with nlme with several
different measures of transpiration taken on each of the 220 trees
grouped in 8 families. The unknown parameters of the model are three +
their variances (and covariances). I want to estimate the variances
among families of the parameters. This would give an idea of the
genetic control of the biological processes modeled.
The question than is:
what would be the conceptual differences between
1) fitting the model with nls on each tree separately and than look at
the variance among families of the parameters with lme
2) carry out one single fitting with nlme using Family/Tree
hierarchical grouping (Tree and Families are considered random factors)
and estimate the within and among families variance of the parameters.
I understand from the Pinheiro and Bates book that there is a degree of
freedom issue, but what are the real advantages and disadvantages of the
This issues is prompted by the fact that it is easy for me to
have results using the first approach but there is no way that
the second approach reaches some kind of convergence: it takes hours and
the it stops without reaching satisfactory results. I have tried several
simplifications of the model with no particular success.
Right now I am using the first approach weighting the parameter with the
inverse of their error estimated with nls. I am using
the weights option with lme using something like:
weight=varFixed(~ 1/vector_of_errors_of_parameter) approach.
Am I out of my mind completely? or am I on a more or less right
Thank you very much to whoever tries to answer this question.
Dipartimento di Scienze Ambientali
Universita` di Parma E-mail: stefano.leonardi _at_ unipr.it
Parco Area delle Scienze 11a Phone : +39-0521-905659
43100 PARMA (Italy) Fax : +39-0521-905402
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