Hello,
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)
return(total.err)
}
SO I'm trying to minimize the total error. This Total Error should be a U shape
essentially.
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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