[R] maximum likelihood problem

[Ravi Varadhan]

Do you want to do a nonlinear least-squares estimation (which is MLE if the
errors are Gaussian)?

If so, you have to define a function that takes the parameter (k) and data
matrix (LR, T, LM), as arguments, and returns a scalar, which is the
residual sum of squares.  Then you can optimize (minimize) that function.

Ravi.

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of mlarkin at rsmas.miami.edu
Sent: Friday, October 01, 2010 4:40 PM
To: r-help at r-project.org
Subject: [R] maximum likelihood problem

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

______________________________________________
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.