Hi Benoit,
your problem is not really a problem of lsoda. The reason of the "crash"
is a violation of the statistical assumptions of least squares
regression due to dependency of residual variance on x. Due to this, K1
is varied over a very large range of values until numeric overflow occurs.
Note that you have an exponentially growing state, so log transformation
will help:
res <- nls(log(foo) ~
log(func(K1)),start=list(K1=1),data=data.frame(foo=y), trace=TRUE)
summary(res)
You may also consider using packages simecol (on CRAN) or FME (on
R-Forge) that both support constrained optimization of ode systems.
Hope it helps
Thomas Petzoldt
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