fpec: AR model Fitting for Control

In timsac: Time Series Analysis and Control Package

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)

timsac documentation built on Sept. 30, 2023, 5:06 p.m.