Hi,
thanks for your message.
You mean to rewrite the function like that:
lw <- function(d, x, im)
{
peri1 <- per(x)
len <- length(x)
m <- len/im
peri <- peri1[2:(m+1)]
z <- c(1:m)
freq <- ((2*pi)/len) * z
result <- log(sum(freq^(2*d-1)*peri))-(2*d)/m * sum(log(freq))
}
and then to use:
target <- function(d) lw(x, d, im=2)
k <- optimize(target, interval=c(0, 0.5))
or just,
k <- optimize(lw, x, im)
But I still get the same values
$minimum
[1] 0.4999542
$objective
[1] 2.739509
after the optimization, no matter my "x" series. It seems that I am still
doing something wrong in the optimize() or in the result() function inside
the loop. But comparing it to the original S-PLUS code, it seems like it is
fine.
Send me if you have any other ideas please.
Thanks again.
fotis
On Sat, Jul 26, 2008 at 12:48 PM, Duncan Murdoch <[EMAIL PROTECTED]>wrote:
> On 26/07/2008 7:40 AM, Fotis Papailias wrote:
>
>> Dear R-users,
>>
>> I have sent another mail some hour ago about a matlab Code I was trying to
>> translate in R.
>>
>> Actually I have found a simpler code originally written in S-PLUS for the
>> same function.
>> Author's page -> http://math.bu.edu/people/murad/methods/locwhitt/
>>
>> =============================================================
>>
>> rfunc_function(h, len, im, peri)
>> # h -- Starting H value for minimization.
>> # len -- Length of time series.
>> # im -- Use only len/im frequencies.
>> # peri -- Periodogram of data.
>> {
>> m <- len %/% im
>> peri <- peri[2:(m + 1)]
>> z <- c(1:m)
>> freq <- (2 * pi)/len * z
>> result <- log(sum(freq^(2 * h - 1) * peri)) - (2 * h)/m *
>> sum(log(freq)
>> ) # cat("H = ", h, "R = ", result, "\n")
>> drop(result)
>> }
>>
>>
>> locwhitt_function(data, h = 0.5, im = 2)
>> # data -- Time series.
>> # h -- Starting H value for minimization.
>> # im -- Use only N/im frequencies where N is length of series.
>>
>> {
>> peri <- per(data)
>> len <- length(data)
>> return(nlminb(start = h, obj = rfunc, len = len, im = im, peri =
>> peri)$
>> parameters)
>> }
>> ===============================================================
>>
>> The author who has written the above S-PLUS code uses two functions (with
>> the locwhitt_function he lets the user to define the data and the
>> parameters
>> and with the rfunc_function he does the minimization.)
>>
>> Mine translation is in R is:
>>
>> where I use a joint function compared to the the above author
>>
>>
>> ================================================================
>>
>> lw <- function(x, d, im)
>> {
>> peri1 <- per(x)
>> len <- length(x)
>> m <- len/im
>> peri <- peri1[2:(m+1)]
>> z <- c(1:m)
>> freq <- ((2*pi)/len) * z
>> result <- log(sum(freq^(2*d-1)*peri))-(2*d)/m * sum(log(freq))
>> }
>>
>> =================================================================
>>
>> which seems to run ok.
>>
>> But when I do
>>
>> k <- optimize(lw, x, im=2, interval=c(0, 0.5))
>>
>> I always get the same result no matter the (simulated) data in x!
>>
>> The parameter of interest to be minimized is "d". Does anyone know how to
>> edit the function "optimize" so it can work properly?
>>
>
> optimize() is fine, but the way you're calling it is not. It optimizes a
> function over the first argument. So you could rewrite lw to put d first,
> or write a new function which calls it, e.g.
>
> target <- function(d) lw(x, d, im)
>
> and then
>
> optimize(target, interval=c(0, 0.5))
>
> Because target is defined in the global environment, it will look there for
> x and im, and you don't need to pass them as arguments: unless x and im
> aren't defined there too!
>
> Duncan Murdoch
>
--
fp
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