[R] Maximum Likelihood Estimation in R

[Henkep]

Dear R-Help,
 
I also send the following post by e-mail to you, however I try to post it
here aswell. My name is Henrik and I am currently trying to solve a Maximum
Likelihood optimization problem in R. Below you can find the output from R,
when I use the "BFGS" method:
 
The problem is that the parameters that I get are very unreasonable, I would
expect the absolute value of each parameter to be bounded by say 5.
(furthermore the variable stdev should be greater than zero).
 
One of the problems seems to be that I need to bound the stdev-variable from
below by zero to avoid the  NaN:s produced. I unfortunately do not know how
to do that.
 
Below "y" is the dataset, to which, I want to fit the parameters. I.e. y is
the vector (or R equivalent) of observations. 
I have also multiplied the log-likelihood function by -1, since I know that
R by default minimizes the objective function. 
 
I would be very happy if you can come up with some Ideas on what is going
wrong in the code below.
 
Thank you for your time!
 
Henrik
 
 
> data<-read.table(file="c:/users/oukal.csv",header=TRUE)
> y<-data$V1
> loglik<-function(estimate,y)
+ {
+ lap<-estimate[1]
+ stdev<-estimate[2]
+ rev<-estimate[3]
+ n<-length(y)
+ 
+ 0.5*n*log(2*pi)+ 0.5*n*log(stdev) +
+ (1/(2*stdev*stdev))*sum(y-(rev/12)-lag(y)*exp(-lap/12))
+ }
> 
> optim(c(4,4,4),loglik,y=y,method="BFGS")
$par
[1] -4884.34155   445.52350    88.53777
$value
[1] -1.910321e+174
$counts
function gradient 
     290        7 
$convergence
[1] 0
$message
NULL
There were 50 or more warnings (use warnings() to see the first 50)

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