> -----Original Message-----
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of wayne.w.jo...@shell.com
> Sent: 22 October 2013 18:52
> To: R-help@r-project.org
> Subject: [R] nls model definition help
>
> Hi fellow R users,
>
> I'm trying to fit a model using nls with the following model
> definition:
>
> y(t+1)=(th1*x1 + R1*x2) * exp(a1*x3) + (1-th1*x1 + R1*x2)*y(t)
>
> ...
>
> Any ideas if this is achievable and if not any ideas on how to fit this
> model?
I'm thinking not, as you have that (t+1) back-reference in there which makes
the calculated y(t) part of the expression. Pretty sure nlm won't handle that
in the formula interface.
However, you could use nlm or optim if you write a function to predict y and
then wrap that in a function to calculate the residual sum of squares (taking
the parameter vector and y[obs] as parameters), then minimise the residual SS.
The hessian from either can then be used to estimate standard errors using the
usual least-squares formulae.
S Ellison
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