canoca {timsac} | R Documentation |
Analyze canonical correlation of an d-dimensional multivariate time series.
canoca(y)
y |
a multivariate time series. |
First AR model is fitted by the minimum AIC procedure. The results are used to ortho-normalize the present and past variables. The present and future variables are tested successively to decide on the dependence of their predictors. When the last DIC (=chi-square - 2.0*N.D.F.) is negative the predictor of the variable is decided to be linearly dependent on the antecedents.
aic |
AIC. |
aicmin |
minimum AIC. |
order.maice |
MAICE AR model order. |
v |
innovation variance. |
arcoef |
autoregressive coefficients. arcoef[i,j,k] shows the value of i-th row, j-th column, k-th order. |
nc |
number of cases. |
future |
number of variable in the future set. |
past |
number of variables in the past set. |
cweight |
future set canonical weight. |
canocoef |
canonical R. |
canocoef2 |
R-squared. |
chisquar |
chi-square. |
ndf |
N.D.F. |
dic |
DIC. |
dicmin |
minimum DIC. |
order.dicmin |
order of minimum DIC. |
matF |
the transition matrix F. |
vectH |
structual characteristic vector H of the canonical Markovian representation. |
matG |
the estimate of the input matrix G. |
vectF |
F matrix in vector form. |
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.
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(1000*3),1000,3) y <- mfilter(x,ar,"recursive") z <- canoca(y) z$arcoef