Mitra
You have a couple of problems. First, if you plot the data (plot(A~Pot))
you'll see there's little chance that an asymptotic, exponential
equation describes your data because they don't rise from zero to Amax
as your model assumes. If Pot is your only independent variable, the
best model I see from looking at the plot is simple Apred = mean(A). It
may be so because the b value is not 0.003 as you use for the starting
point, but a much larger number so that the rise to the asymptote has
occurred before data collection began, so that your A variable
represents Amax with a lot of scatter. Second, and more important, even
though the model 'may be' correct (always an assumption in nls that must
be tested post hoc), your experiments appear to contain no independent
information concerning the values of the parameters. This would yield
the error message you are getting from nls, which is really saying that
the sum-of-squares surface is insensitive to changes in Amax and b so
the search algorithm is lost trying to find a minimum value that likely
doesn't exist. You are a long way from worrying about whether to use
plinear - you're problem is more fundamental. Plot the data, then plot
a line that follows your model and ask yourself whether the model looks
like the data.
Good luck
David Stevens
On 10/24/2011 8:47 AM, mitra Rahmati wrote:
> mod = nls(A~ Amax * (1-exp(-b*Pot)),start=list(Amax=10,b=0.003))
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