[R] Optim function returning always initial value for parameter to be optimized

[ProfJCNash]

Did you check the gradient? I don't think so. It's zero, so of course
you end up where you start.

Try

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

soln <- optim(par = 300, fn=err.th.scalar, data = data.input, method =
"BFGS")
soln
require("numDeriv")
gtest <- grad(err.th.scalar, x=300, data = data.input)
gtest


On 2018-02-09 09:05 AM, BARLAS Marios 247554 wrote:
> 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")


Maybe develop an analytic gradient if it is very small, as the numeric
approximation can then be zero even when the true gradient is not.

JN