I'm trying to fminimize the following problem:

You have a data frame with 2 columns.

data.input= data.frame(state1 = (1:500), state2 = (201:700) )

with data that partially overlap in terms of values. 

I want to minimize the assessment error of each state by using this function:

err.th.scalar <- function(threshold, data){
  state1 <- data$state1
  state2 <- data$state2
  op1l <- length(state1)
  op2l <- length(state2)
  op1.err <- sum(state1 <= threshold)/op1l
  op2.err <- sum(state2 >= threshold)/op2l
  total.err <- (op1.err + op2.err)


SO I'm trying to minimize the total error. This Total Error should be a U shape 

I'm using optim as follows: 

optim(par = 300, fn=err.th.scalar, data = data.input, method = "BFGS")

For some reason that's driving me crazy, in the first trial it worked but right 
now the output of optim for the parameter to get optimized is EXACTLY the same 
as the initial estimate whatever the initial estimate value is. 

Please, any ideas why ? 

I can't see the error at this moment.

Thanks in advance,
Marios Barlas
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