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
> 2. Here is (I think) the best code that I've done so far even though it
> crashes
> when I call nls()
>
>
Not sure, but I believe you have taken the advice to produce reproducible
code too seriously and got trapped by the bold warning in nls:
Warning
Do not use nls on artificial "zero-residual" data.
The nls function uses a relative-offset convergence criterion that compares
the numerical imprecision at the current parameter estimates to the residual
sum-of-squares. This performs well on data of the form
...
Creating some noise in your data might help
Dieter
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