On 2012-04-01 17:31, n.surawski wrote:> Greetings,
>
> I am having some troubles with the nls() function in R V 2.14.2. I am
doing
> some modelling where I want to predict the mass of leaf litter on the
forest
> floor (X) as a function of time since fire (t). Fortunately, I have a
> differential equation that I can fit to the data which is acceptable on
> theoretical grounds. It is: X(t) = (L/k)[1-exp(-kt)], where L is the
litter
> fall rate (t/ha/yr) and k is the decomposition rate (/yr). I have two
> problems:
>
> (1) I have experimental error in both X and t. Is there a way to take this
> into account with nls?
> (2) Is there a way to constrain the parameter estimates from nls?
>
> For example, for a data snippet:
> X = 4.6 4.1 4.7 11.0
> t = 1.5 4.5 7.0 8.0
>
> After I run nls I get:
> L = 0.873
> k = -0.059
>
> The estimate for L is ok, but k (by definition) should be greater than 0.
>
> Is there a way around this?
Yes.
Plot your data, decide which you trust more: your data or theory.
There is no way to use the given data to help substantiate
the proposed theory.
As to your other questions above:
(1) If the uncertainty in your t values is small compared
with that in the X values, then I would just ignore it.
(2) To force a parameter to be positive, see ?SSasymp or
for your case, perhaps ?SSasympOrig.
Peter Ehlers
>
> Many thanks,
>
> Nic Surawski.
>
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