optsim {timsac} | R Documentation |
Perform optimal control simulation and evaluate the means and variances of the controlled and manipulated variables X and Y.
optsim(y, max.order = NULL, ns, q, r, noise = NULL, len, plot = TRUE)
y |
a multivariate time series. |
max.order |
upper limit of model order. Default is 2*sqrt(n), where n is the length of the time series |
ns |
number of steps of simulation. |
q |
positive definite matrix Q. |
r |
positive definite matrix R. |
noise |
noise. If not provided, Gaussian vector white noise with the
length |
len |
length of white noise record. |
plot |
logical. If |
trans |
first m columns of transition matrix, where m is the number of controlled variables. |
gamma |
gamma matrix. |
gain |
gain matrix. |
convar |
controlled variables X. |
manvar |
manipulated variables Y. |
xmean |
mean of X. |
ymean |
mean of Y. |
xvar |
variance of X. |
yvar |
variance of Y. |
x2sum |
sum of X^2. |
y2sum |
sum of Y^2. |
x2mean |
mean of X^2. |
y2mean |
mean of Y^2. |
H.Akaike and T.Nakagawa (1988) Statistical Analysis and Control of Dynamic Systems. Kluwer Academic publishers.
# Multivariate Example Data 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") q.mat <- matrix(c(0.16,0,0,0.09), nrow = 2, ncol = 2) r.mat <- as.matrix(0.001) optsim(y, max.order = 10, ns = 20, q = q.mat, r = r.mat, len = 20)