optimization using "nlminb"

im now performing Location Model using non-parametric smoothing to estimate the paramneters.....one of the smoothed paramater is the lamdha that i have to optimize...

so in that case, i decide to use "nlminb function" to achieve it.....

however, my programing give me the same "$par" value even though it was iterate 150 time and make 200 evaluation (by default)..... which is it choose "the start value as $par" (that is 0.000001 ...... i think, there must be something wrong with my written program....

my programing look like:- (note: w is the parameter that i want to optimize and LOO is stand for leave-one-out

BEGIN

Myfunc <- function(w, n1, n2, v1, v2, g)
{  ## open  loop for main function

## DATA generation
        # generate data from group 1 and 2
        # for each group: discretise the continuous to binary
        # newdata <- combine the groups 1 and 2

## MODEL construction
     countError <- 0
        n <- nrow(newdata)

       for (k in 1:n)
       {# open loop for leave-one-out
             # construct model based on n-1 object using smoothing method
                 # classify omitted object
                countError <- countError + countE
       }   # close loop for LOO process

          Error <- countError / n     # error rate counted from LOO procedure 

     return(Error)           # The Average ERROR Rate from LOO procedure 

}       # close loop for Myfunc

library(stats)
  nlminb(start=0.000001, Myfunc, lower=0.000001, upper=0.999999, 
                 control=list(eval.max=100,开发者_开发技巧 iter.max=100))

END

could someone help me......

your concerns and guidances is highly appreciated and really100 needed......

Hashibah, Statistic PhD Student

In your question, provide a nlminb with a univariate starting value. If you are doing univariate optimisation, it is probably worth looking at optimize. If your function is multivariate, then you need to call nlminb slightly differently.

You need define the objective function such that you provide the parameters to optimize over as a vector which is the first argument. Other inputs to the objective function should be provided as subsequent arguments.

For example (modified from the nlminb help page):

X <- rnbinom(100, mu = 10, size = 10)
hdev <- function(par, x) {
 -sum(dnbinom(x, mu = par[1], size = par[2], log = TRUE))
}
nlminb(start = c(9, 12), hdev, x = X)