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
write the function "optimize" so it can work properly?
Thanks again.
Fotis
--
fp
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