Hi,
It isn't surprising that convergence results depend on initial par
values in a nonlinear optimization problem. There might be local maxima
in the landscape defined by your likelihood function and you have to
explore it for the proper starting places.
One suggestion is to try many different sets of initial values and
select the best ones by, for each convergence result, examining the
value of the maximized function, the gradients of the parameters at the
maximized value, the standard errors of the parameters, the correlation
matrix between parameter estimates.
Also, if you use optimx (from package optimx) as your wrapper, you can
check the Karush-Kuhn-Tucker (KKT) conditions, and try several numerical
methods (not just CG) in one go.
HTH
Ruben
On 6/28/2012 12:54 PM, nata...@orchidpharma.com wrote:
Dear list-members,
I have done optimization of 3 parameters by maximum likelihood method using
conjugate gradient as optimizer.
Since I have the reported value of the parameters from an article, I can
validate the result of the optimized
parameters. The problem is that optimizer converges to the desirable
value. (as reported in the article) only
for a certain starting value.
If the staring value of the parameters are like (2,1,1) then the value of
parameters the function converges is
$par
[1] -0.4169408 -0.2800828 2.9614670
And the value is close to the value of article reported in the article
Vs = 0.4861 ; Vn = 0.1478 and m is some positive value (no value mentioned in
the article)
And If I fine tune the starting value from (2,1,1) to (1.949,1.13,1) then I get
the value of the parameters
very close to the one reported.
$par
[1] -0.4700892 -0.1428245 2.9614670
Now the question is how I can find the starting values for the test experiment
for which I am going to implement
this optimization procedure.
Is there any function to be wrapped with optim() to find the right starting
value.
Regards,
B.Nataraj
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