Dear R-Help,

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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