mulnos {timsac}

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)