Dear friends.
I use nls() and encounter the following puzzling problem:
I have a function f(a,b,c,x), I have a data vector of x and a vectory y of
realized value of f.
Case1
I tried to estimate c with (a=0.3, b=0.5) fixed:
nls(y~f(a,b,c,x), control=list(maxiter = 100000, minFactor=0.5
^2048),start=list(c=0.5)).
The error message is: "number of iterations exceeded maximum of 100000"
Case2
I then think maybe the value of a and be are not reasonable. So, I let nls()
estimate (a,b,c) altogether:
nls(y~f(a,b,c,x), control=list(maxiter = 100000, minFactor=0.5
^2048),start=list(a=0.3,b=0.5,c=0.5)).
The error message is:
"singular gradient matrix at initial parameter estimates".
This is what puzzles me, if the initial parameter of (a=0.3,b=0.5,c=0.5) can
create 'singular gradient matrix', then why doesn't this 'singular gradient
matrix' appear in Case1?
I have tried to change the initial value of (a,b,c) around but the problem
persists. I am wondering if there is a way out.
My another question is, I need to run 220 of nls() in my program with
different y and x. When one of the nls() encounter a problem, the whole
program stops. In my case, the 3rd nls() runs into a problem. I would
still need the program to run the remaining 217 nls( )! Is there a way to
make the program skip the problematic nls() and complete the ramaining
nls()'s?
Your help will be highly appreciated!
Yuchen Luo
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