[R] [Fwd: RE: maximum likelihood problem]
mlarkin at rsmas.miami.edu
mlarkin at rsmas.miami.edu
Sat Oct 2 03:11:08 CEST 2010
I forgot to add that I first gave a starting value for K.
Nonlinear least squares won't work because my errors are not normally
distributed.
Any advide on my maximum likelihood function would be greatly appreciated.
---------------------------- Original Message ----------------------------
Subject: RE: [R] maximum likelihood problem
From: "Ravi Varadhan" <rvaradhan at jhmi.edu>
Date: Fri, October 1, 2010 5:10 pm
To: mlarkin at rsmas.miami.edu
r-help at r-project.org
--------------------------------------------------------------------------
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
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