R: AR model Fitting for Control

fpec {timsac} | R Documentation

## AR model Fitting for Control

### Description

Perform AR model fitting for control.

### Usage

      fpec(y, max.order = NULL, control = NULL, manip = 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`.
`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.

### Value

`cov` | covariance matrix rearrangement.
`fpec` | FPEC (AR model fitting for control).
`rfpec` | RFPEC.
`aic` | AIC.
`ordermin` | order of minimum FPEC.
`fpecmin` | minimum FPEC.
`rfpecmin` | minimum RFPEC.
`aicmin` | minimum AIC.
`perr` | prediction error covariance matrix.
`arcoef` | a set of coefficient matrices. `arcoef[i,j,k]` shows the value of i-th row, j-th column, k-th order.

### 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")
    fpec(y, max.order = 10)

[Package timsac version 1.3.6 Index]