Description Usage Arguments Value

This function is modified from original CCA function for two reasons: to deal with only positive eigenvalues larger than the tolerance when calculating the inverse of the matrices and to compuate Singular Value Decomposition using `irlba`

algorithm. Inputs should be correlation or covariance matrices of each data set and between datasets. This function returns only the first pair of canonical covariates.

1 | ```
standardCCA(S1, S2, S12, tol = 1e-04)
``` |

`S1` |
correlation/covariance matrix of dataset |

`S2` |
correlation/covariance matrix of dataset |

`S12` |
correlation/covariance matrix between dataset |

`tol` |
tolerance for eigenvalues. |

`standardCCA`

returns a data.frame containing

cancor: estimated canonical correlation.

w1: estimated canonical direction

*w1*.w2: estimated canonical direction

*w2*.

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