mulnos {timsac} | R Documentation |
Compute relative power contributions in differential and integrated form, assuming the orthogonality between noise sources.
mulnos(y, max.order=NULL, ncon=NULL, nman=0, h, inw=NULL)
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. |
nperr |
a normalized prediction error covaiance matrix. |
diffr |
differential relative power contribution. |
integr |
integrated relative power contribution. |
H.Akaike and T.Nakagawa (1988) Statistical Analysis and Control of Dynamic Systems. Kluwer Academic publishers.
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)