[R] Likelihood Function for Multinomial Logistic Regression and its partial derivatives
nikolay12 at gmail.com
Sun Aug 2 21:39:33 CEST 2009
Thanks a lot. The info about computing the gradient will be helpful.
I admit that I am somewhat confused about the likelihood function itself. It
is often said that you need to set a reference category. However, I found
two different implementations in Matlab for which setting the reference
category is not really obvious. Thus I wanted to find a single, indisputable
implementation of the likelihood function for multinomous logistic
regression in R. Can't believe there is no such available.
Ravi Varadhan wrote:
> Providing the gradient function is generally a good idea in optimization;
> however, it is not necessary. Almost all optimization routines will
> compute this using a simple finite-difference approximation, if they are
> not user-specified. If your function is very complicated, then you are
> more likely to make a mistake in computing analytic gradient, although
> many optimization routines also provide a check to see if the gradient is
> correctly specified or not. But you can do this yourself using the `grad'
> function in "numDeriv" package.
> Hope this is helpful,
> Ravi Varadhan, Ph.D.
> Assistant Professor,
> Division of Geriatric Medicine and Gerontology
> School of Medicine
> Johns Hopkins University
> Ph. (410) 502-2619
> email: rvaradhan at jhmi.edu
> ----- Original Message -----
> From: nikolay12 <nikolay12 at gmail.com>
> Date: Sunday, August 2, 2009 3:04 am
> Subject: [R] Likelihood Function for Multinomial Logistic Regression and
> its partial derivatives
> To: r-help at r-project.org
>> I would like to apply the L-BFGS optimization algorithm to compute
>> the MLE
>> of a multilevel multinomial Logistic Regression.
>> The likelihood formula for this model has as one of the summands the
>> for computing the likelihood of an ordinary (single-level)
>> multinomial logit
>> regression. So I would basically need the R implementation for this
>> The L-BFGS algorithm also requires computing the partial derivatives
>> of that
>> formula in respect to all parameters. I would appreciate if you can
>> point me
>> to existing implementations that can do the above.
>> PS. The long story for the above:
>> My data is as follows:
>> - a vector of observed values (lenght = D) of the dependent multinomial
>> variable each element belonging to one of N levels of that variable
>> - a matrix of corresponding observed values (O x P) of the independent
>> variables (P in total, most of them are binary but also a few are
>> - a vector of current estimates (or starting values) for the Beta
>> coefficients of the independent variables (length = P).
>> This data is available for 4 different pools. The partially-pooled model
>> that I want to compute has as a likelihood function a sum of several
>> elements, one being the classical likelihood function of a
>> multinomial logit
>> regression for each of the 4 pools.
>> This is the same model as in Finkel and Manning "Hierarchical Bayesian
>> Domain Adaptation" (2009).
>> View this message in context:
>> Sent from the R help mailing list archive at Nabble.com.
>> R-help at r-project.org mailing list
>> PLEASE do read the posting guide
>> and provide commented, minimal, self-contained, reproducible code.
> R-help at r-project.org mailing list
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
View this message in context: http://www.nabble.com/Likelihood-Function-for-Multinomial-Logistic-Regression-and-its-partial-derivatives-tp24772731p24781298.html
Sent from the R help mailing list archive at Nabble.com.
More information about the R-help