[R] maximum likelihood problem

[Prof. John C Nash]

Ravi has already responded about the possibility of using nls(). He and I also have put up 
the optimx package which allows a control 'maximize=TRUE' because of the awkwardness of 
using fnscale in optim. (optimx still lets you use optim()'s tools too, but wrapped with 
this facility.) There are a number of optimizers available through optimx. If you can 
compute gradients analytically, you will find methods that can use these much more 
efficient, and your subsequent Hessian estimates better too.

You should be aware that optimise() is for 1D problems i.e., 1 parameter to optimize.

John Nash


> Message: 81
> Date: Fri, 1 Oct 2010 16:39:58 -0400 (EDT)
> From: mlarkin at rsmas.miami.edu
> To: r-help at r-project.org
> Subject: [R] maximum likelihood problem
> Message-ID:
> 	<3675.129.171.104.122.1285965598.squirrel at webmail.rsmas.miami.edu>
> Content-Type: text/plain;charset=iso-8859-1
>
> I am trying to figure out how to run maximum likelihood in R.  Here is my
> situation:
>
> I have the following equation:
> equation<-(1/LR-(exp(-k*T)*LM)*(1-exp(-k)))
>
> LR, T, and LM are vectors of data.  I want to R to change the value of k
> to maximize the value of equation.
>
> My attempts at optim and optimize have been unsuccessful.  Are these the
> recommended functions that I should use to maximize my equation?
>
> With optim I wanted the function to be maximized so I had to make the
> fnscale negative.  Here is what I put:
>
> L<-optim(k,equation,control=(fnscale=-1))
>
> My result:   Error: could not find function "fn"
>
>
> Here is what I put for optimize:
>
> L<-optimise(equation,k,maximum=TRUE)
>
> My result:   Error: 'xmin' not less than 'xmax'
>
> Any advise would be greatly appreciated.
> Mike
>
>
>
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