bbouling wrote:
Thanks to Dieter Menne and Spencer Graves I started to get my way through
lsoda()
Now I need to use it in with nls() to assess parameters
I have a go with a basic example
dy/dt = K1*conc
I try to assess the value of K1 from a simulated data set with a K1 close
to
Thanks to Dieter Menne and Spencer Graves I started to get my way through
lsoda()
Now I need to use it in with nls() to assess parameters
I have a go with a basic example
dy/dt = K1*conc
I try to assess the value of K1 from a simulated data set with a K1 close to
2.
Here is (I think) the best
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.
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