mulnos: Relative Power Contribution

In timsac: Time Series Analysis and Control Package

Relative Power Contribution

Description

Compute relative power contributions in differential and integrated form, assuming the orthogonality between noise sources.

Usage

  mulnos(y, max.order = NULL, control = NULL, manip = NULL, h)

Arguments

y	a multivariate time series.
max.order	upper limit of model order. Default is 2 \sqrt{n}, where n is the length of time series y.
control	controlled variables. Default is c(1:d), where d is the dimension of the time series y.
manip	manipulated variables. Default number of manipulated variable is '0'.
h	specify frequencies i/2h (i=0, \ldots ,h).

Value

nperr	a normalized prediction error covariance matrix.
diffr	differential relative power contribution.
integr	integrated relative power contribution.

References

H.Akaike and T.Nakagawa (1988) Statistical Analysis and Control of Dynamic Systems. Kluwer Academic publishers.

Examples

ar <- array(0, dim = c(3,3,2))
ar[, , 1] <- matrix(c(0.4,  0,   0.3,
                      0.2, -0.1, -0.5,
                      0.3,  0.1, 0), nrow = 3, ncol = 3, byrow = TRUE)
ar[, , 2] <- matrix(c(0,  -0.3,  0.5,
                      0.7, -0.4,  1,
                      0,   -0.5,  0.3), nrow = 3, ncol = 3, byrow = TRUE)
x <- matrix(rnorm(200*3), nrow = 200, ncol = 3)
y <- mfilter(x, ar, "recursive")
mulnos(y, max.order = 10, h = 20)

timsac documentation built on Sept. 30, 2023, 5:06 p.m.