R: Relative Power Contribution

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, 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/2`h` (i=0, … ,`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)

[Package timsac version 1.3.6 Index]