mulnos {timsac} | R Documentation |
Compute relative power contributions in differential and integrated form, assuming the orthogonality between noise sources.
mulnos(y, max.order = NULL, control = NULL, manip = NULL, h)
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
|
control |
controlled variables. Default is c(1:d), where d is
the dimension of the time series |
manip |
manipulated variables. Default number of manipulated variable is '0'. |
h |
specify frequencies i/2 |
nperr |
a normalized prediction error covariance 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), 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)