mulnos {timsac}R Documentation

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, ncon=NULL, nman=0, h, inw=NULL)

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.
ncon number of controlled variables. Default is d, where d is the dimension of the time series y.
nman number of maninpulated variables.
h specify frequencies i/2h (i=0,...,h).
inw indicator; inw[i] (i=1,...,ncon) indicate the controlled variables and
inw[i+ncon] (i=1,...,nman) indicate the manipulate variables.

Value

nperr a normalized prediction error covaiance 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),3,3,byrow=TRUE)
  ar[,,2] <- matrix(c(0,  -0.3,  0.5,
                      0.7, -0.4,  1,
                      0,   -0.5,  0.3),3,3,byrow=TRUE)
  x <- matrix(rnorm(200*3),200,3)
  y <- mfilter(x,ar,"recursive")
  mulnos(y, max.order=10, ncon=3, nman=0, h=20)

[Package timsac version 1.2.1 Index]