Description Usage Arguments Value References

Internal mixedCCA function finding w1 and w2 given R1, R2 and R12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
find_w12bic(
n,
R1,
R2,
R12,
lamseq1,
lamseq2,
w1init,
w2init,
BICtype,
maxiter = 100,
tol = 0.01,
trace = FALSE,
lassoverbose = FALSE
)
``` |

`n` |
Sample size |

`R1` |
Correlation matrix of dataset |

`R2` |
Correlation matrix of dataset |

`R12` |
Correlation matrix between the dataset |

`lamseq1` |
A sequence of lambda values for the datasets |

`lamseq2` |
A sequence of lambda values for the datasets |

`w1init` |
An initial vector of length p1 for canonical direction |

`w2init` |
An initial vector of length p1 for canonical direction |

`BICtype` |
Either 1 or 2: For more details for two options, see the reference. |

`maxiter` |
The maximum number of iterations allowed. |

`tol` |
The desired accuracy (convergence tolerance). |

`trace` |
If |

`lassoverbose` |
If |

`find_w12bic`

returns a data.frame containing

w1: estimated canonical direction

*w1*.w2: estimated canonical direction

*w2*.w1trace: a matrix, of which column is the estimated canonical direction

*w1*at each iteration. The number of columns is the number of iteration until the convergence.w2trace: a matrix, of which column is the estimated canonical direction

*w2*at each iteration. The number of columns is the number of iteration until the convergence.lam1.iter: For each iteration, what lambda value is selected for

*w1*is stored.lam2.iter: For each iteration, what lambda value is selected for

*w2*is stored.obj: objective function value without penalty:

*w1^T * R12 * w2*. If lamseq1 and lamseq2 are scalar, then original objective function including penalty part will be used.

Yoon G., Carroll R.J. and Gaynanova I. (2020) "Sparse semiparametric canonical correlation analysis for data of mixed types" <doi:10.1093/biomet/asaa007>.

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