timsac (version 1.3.8)

mulnos: 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)

Value

nperr

a normalized prediction error covariance matrix.

diffr

differential relative power contribution.

integr

integrated relative power contribution.

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 \(i/2\)h (\(i=0, \ldots ,\)h).

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)