Description Usage Arguments Value Author(s) Examples

This function applies a Simultaneous Component Analysis (SCA). The idea is that the scores for both blocks should have a similar behaviour if the components are in the common mode. Evaluation is by the ratios between the explained variances (SSQ) of each block and the estimator. The highest component count with 0.8 < ratio < 1.5 is selected.

1 | ```
selectCommonComps(X, Y, Rmax)
``` |

`X` |
Matrix of omics data |

`Y` |
Matrix of omics data |

`Rmax` |
Maximum number of common components to find |

A list with components:

- common
Optimal number of common components

- ssqs
Matrix of SSQ for each block and estimator

- pssq
`ggplot`

object showing SSQ for each block and estimator- pratios
`ggplot`

object showing SSQ ratios between each block and estimator

Patricia Sebastian-Leon

1 2 3 4 5 | ```
data(STATegRa_S3)
cc <- selectCommonComps(X=Block1.PCA, Y=Block2.PCA, Rmax=3)
cc$common
cc$pssq
cc$pratios
``` |

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