optsim {timsac} | R Documentation |
Optimal Control Simulation
Description
Perform optimal control simulation and evaluate the means and variances of the controlled and manipulated variables X and Y.
Usage
optsim(y, max.order=NULL, ns, q, r, noise=NULL, len, plot=TRUE)
Arguments
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 y. |
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 is generated. |
len |
length of white noise record. |
plot |
logical. If TRUE (default) controlled variables X and manipulated variables Y are plotted. |
Value
trans |
first ir columns of transition matrix, where ir 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. |
References
H.Akaike and T.Nakagawa (1988) Statistical Analysis and Control
of Dynamic Systems. Kluwer Academic publishers.
Examples
# 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),3,3,byrow=TRUE)
ar[,,2] <- matrix(c(0, -0.3, 0.5,
0.7, -0.4, 1,
0, -0.5, 0.3),3,3,byrow=TRUE)
x <- matrix(rnorm(200*3),200,3)
y <- mfilter(x,ar,"recursive")
q <- matrix(c(0.16,0,0,0.09), 2, 2)
r <- matrix(0.001, 1, 1)
optsim(y, max.order=10, ns=20, q, r, len=20)
[Package
timsac version 1.2.1
Index]