fpec {timsac} | R Documentation |
Perform AR model fitting for control.
fpec(y, max.order = NULL, control = NULL, manip = 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
|
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. |
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. |
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") fpec(y, max.order = 10)