canoca {timsac}R Documentation

Canonical Correlation Analysis of Vector Time Series

Description

Analyze canonical correlation of an 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 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.

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),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

[Package timsac version 1.2.1 Index]