markov {timsac} | R Documentation |
Compute maximum likelihood estimates of Markovian model.
markov(y, tmp.file=NULL)
y |
a multivariate time series. |
tmp.file |
temporary file name. If NULL (default) output no file. |
This function is usually used with "simcon".
id |
id[i]=1 means that the i-th row of F contains free parameters. |
ir |
ir[i] denotes the position of the last non-zero element within the i-th row of F. |
ij |
ij[i] denotes the position of the i-th non-trivial row within F. |
ik |
ik[i] denotes the number of free parameters within the i-th non-trivial row of F. |
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. arcoef[i,j,k] shows the value of i-th row, j-th column, k-th order. |
impuls |
impulse response matrices. |
macoef |
MA coefficient matrices. macoef[i,j,k] shows the value of i-th row, j-th column, k-th order. |
v |
inovation 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),1000,2) ma <- array(0,dim=c(2,2,2)) ma[,,1] <- matrix(c( -1.0, 0.0, 0.0, -1.0), 2,2,byrow=TRUE) ma[,,2] <- matrix(c( -0.2, 0.0, -0.1, -0.3), 2,2,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), 2,2,byrow=TRUE) ar[,,2] <- matrix(c( -0.5, -0.2, -0.2, -0.5), 2,2,byrow=TRUE) ar[,,3] <- matrix(c( -0.3, -0.05, -0.1, -0.30), 2,2,byrow=TRUE) z <- mfilter(y,ar,"recursive") markov(z)