On 02/10/2018 06:00 AM, r-help-requ...@r-project.org wrote:
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
I think this function is not smooth, and not even continuous. Gradient
methods require differentiable (smooth) functions. A numerical
approximation will be zero unless you are right near a jump point, so
you are unlikely to move from your initial guess.
Paul
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
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