[R] MLE optimization
Ravi Varadhan
RVaradhan at jhmi.edu
Tue Jan 5 00:17:34 CET 2010
"should write a function that uses the parameters and the sample data as
input and outputs the likelihood. Is it correct?"
Yes, that is correct. Take a look at the optim() function. ?optim
What type of convergence problems did you experience with Matlab? I am not
sure if using R can overcome fundamental modeling and computational issues,
such as over-specification of the model for the data at hand. But, may be
you need to impose constraints on the parameter if you are fitting a
Gaussian mixture.
Another option is to use packages that are specially designed to model
finite mixtures such as "flexmix" or "mixtools".
Ravi.
----------------------------------------------------------------------------
-------
Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: rvaradhan at jhmi.edu
Webpage:
http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h
tml
----------------------------------------------------------------------------
--------
-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of jckval
Sent: Monday, January 04, 2010 5:53 PM
To: r-help at r-project.org
Subject: [R] MLE optimization
Folks,
I'm kind of newbie in R, but with some background in Matlab and VBA
programming. Last month I was implementing a Maximum Likelihood Estimation
in Matlab, but the algorithms didn't converge. So my academic advisor
suggested using R. My problem is: estimate a mean reverting jump diffusion
parameters. I've succeeded in deriving the likelihood function (which looks
like a gaussian mixture) and it is implemented in R. My main doubts are
related to the inputs and outputs that this function should generate, for
instance, in Matlab this function should get the parameters as input and
output the likelihood using the sample data (imported within the function).
In order to make R optimizers to work I, apparently, should write a function
that uses the parameters and the sample data as input and outputs the
likelihood. Is it correct?
Could someone reply with an example code which examplifies the type of
function I should write and the syntax to optimize?
Alternatively, could anyone suggest a good MLE tutorial and package?
Thankfully,
JC
--
View this message in context:
http://n4.nabble.com/MLE-optimization-tp998655p998655.html
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
R-help at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
More information about the R-help
mailing list