markov {timsac} | R Documentation |
Compute maximum likelihood estimates of Markovian model.
markov(y)
y |
a multivariate time series. |
This function is usually used with simcon
.
id |
|
ir |
|
ij |
|
ik |
|
grad |
gradient vector. |
matFi |
initial estimate of the transition matrix F. |
matF |
transition matrix F. |
matG |
input matrix G. |
davvar |
DAVIDON variance. |
arcoef |
AR coefficient matrices. |
impulse |
impulse response matrices. |
macoef |
MA coefficient matrices. |
v |
innovation variance. |
aic |
AIC. |
H.Akaike, E.Arahata and T.Ozaki (1975) Computer Science Monograph, No.5, Timsac74, A Time Series Analysis and Control Program Package (1). The Institute of Statistical Mathematics.
x <- matrix(rnorm(1000*2), nrow = 1000, ncol = 2) ma <- array(0, dim = c(2,2,2)) ma[, , 1] <- matrix(c( -1.0, 0.0, 0.0, -1.0), nrow = 2, ncol = 2, byrow = TRUE) ma[, , 2] <- matrix(c( -0.2, 0.0, -0.1, -0.3), nrow = 2, ncol = 2, byrow = TRUE) y <- mfilter(x, ma, "convolution") ar <- array(0, dim = c(2,2,3)) ar[, , 1] <- matrix(c( -1.0, 0.0, 0.0, -1.0), nrow = 2, ncol = 2, byrow = TRUE) ar[, , 2] <- matrix(c( -0.5, -0.2, -0.2, -0.5), nrow = 2, ncol = 2, byrow = TRUE) ar[, , 3] <- matrix(c( -0.3, -0.05, -0.1, -0.30), nrow = 2, ncol = 2, byrow = TRUE) z <- mfilter(y, ar, "recursive") markov(z)