canoca {timsac}R Documentation

Canonical Correlation Analysis of Vector Time Series

Description

Analyze canonical correlation of a d-dimensional multivariate time series.

Usage

canoca(y)

Arguments

y

a multivariate time series.

Details

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.

Value

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 ii-th row, jj-th column, kk-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 FF.

vectH

structural characteristic vector HH of the canonical Markovian representation.

matG

the estimate of the input matrix GG.

vectF

matrix FF in vector form.

References

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.

Examples

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(1000*3), nrow = 1000, ncol = 3)
y <- mfilter(x, ar, "recursive")
z <- canoca(y)
z$arcoef

[Package timsac version 1.3.8-4 Index]