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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