R: Canonical Correlation Analysis of Vector Time Series

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 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` | structural characteristic vector H of the canonical Markovian representation.
`matG` | the estimate of the input matrix G.
`vectF` | matrix F 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 1.3.5 Index]