Description Usage Arguments Details Value References See Also Examples

Obtain upper and lower bounds of tuning parameters for each canonical correlation vector. It is recommended to use cvselpscca to choose optimal tuning paramters for each dataset.

1 2 | ```
cvtunerange(Xdata1=Xdata1,Xdata2=Xdata2,ncancorr=ncancorr,
CovStructure="Iden",standardize=TRUE)
``` |

`Xdata1` |
A matrix of size |

`Xdata2` |
A matrix of size |

`ncancorr` |
Number of canonical correlation vectors. Default is one. |

`CovStructure` |
Covariance structure to use in estimating sparse canonical correlation vectors. Either "Iden" or "Ridge". Iden assumes the covariance matrix for each dataset is identity. Ridge uses the sample covariance for each dataset. See reference article for more details. |

`standardize` |
TRUE or FALSE. If TRUE, data will be normalized to have mean zero and variance one for each variable. Default is TRUE. |

The function will return tuning ranges for sparse estimation of canonical correlation vectors. To see the results, use the “$" operator.

`TauX1range` |
A |

`TauX2range` |
A |

Sandra E. Safo, Jeongyoun Ahn, Yongho Jeon, and Sungkyu Jung (2018) , *Sparse Generalized Eigenvalue Problem with Application
to Canonical Correlation Analysis for Integrative Analysis
of Methylation and Gene Expression Data*. *Biometrics*

1 | ```
#see example in multiplescca
``` |

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