[R] Strating value problem for CG in optim()

[nataraj at orchidpharma.com]

Thanks Ruben and John for replying to me. I am trying Rcgmin as suggested by John Nash with numerical gradient but the result converged so for not close to what I got in CG implemented in optim( ) and I have to find the analytical gradient equation for multivariate Gaussian distribution. Is this function available in R already?

Ruben , In my current approach, all possible starting value of parameters converges to the value of objective function to $value 23.25272 however I am trying to look up to other parameters to justify the predicted parameters.

I have one more doubts, my objective function returning negative value to optim() in order to consider it as maximizer, does that mean it also return the parameter as negative value ?

Thanks!
B.Nataraj



-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Ruben
Sent: Thursday, June 28, 2012 4:18 PM
To: r-help at r-project.org
Subject: Re: [R] Strating value problem for CG in optim()

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, nataraj at 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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>

--
Ruben H. Roa-Ureta, Ph. D.
Senior Scientist
Marine Studies Section, Center for Environment and Water,
Research Institute, King Fahd University of Petroleum and Minerals,
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