specFGN {longmemo} | R Documentation |
Calculation of the spectral density f of normalized fractional Gaussian noise with self-similarity parameter H at the Fourier frequencies 2*pi*j/m (j=1,...,(m-1)).
specFGN(eta, m, nsum = 200)
eta |
parameter vector eta = c(H, *) . |
m |
sample size determining Fourier frequencies. |
nsum |
length of approximating Riemans sum. |
Note that
an object of class "spec"
(see also spectrum
)
with components
freq |
the Fourier frequencies (in (0,π)) at which the spectrum is computed. |
spec |
the scaled values spectral density f(λ)
values at the freq values of λ.f*(lambda) = f(lambda) / theta1 adjusted such int log(f^*(λ)) dλ = 0. |
theta1 |
the scale factor theta_1. |
H |
the self-similarity parameter from input. |
method |
a character indicating the kind of model used. |
Jan Beran (principal) and Martin Maechler (fine tuning)
Jan Beran (1994). Statistics for Long-Memory Processes; Chapman & Hall, NY.
str(r.7 <- specFGN(0.7, m = 100)) str(r.7f <- specFGN(0.7, m = 100, nsum = 10000)) all.equal(r.7, r.7f)# different in about 5th digit only str(r.5 <- specFGN(0.5, m = 100)) try(plot(r.7)) ## work around plot.spec() `bug' in R < 1.6.0 plot(r.5, add = TRUE, col = "blue")