Description Usage Arguments Value Note References Examples

Use PCA-GCA method to identify the number of common and distinctive components.

1 | ```
pca_gca(DATA, Jk, cor_min, return_scores)
``` |

`DATA` |
A concatenated data matrix with the same number of rows. |

`Jk` |
A vector containing number of variables in the concatinated data matrix. Please see the example below. |

`cor_min` |
The minimum correlation between two components. The default value is .7; thus, it means that if the correlation between the two component is at least .7, then these two components are regarded as forming a single common component. |

`return_scores` |
If TRUE, then the function will return the component scores for each block for further analysis. |

It prints out the number of components of each block and the number of common components. It also returns the component scores for each block for further analysis, if `return_scores = TRUE`

.

Please be ware of the interactive input: The function first performs PCA on each data block and then displays the eigenvalues (and a scree plot). Afterwards the function awaits the input from the user - it needs to know how many components need to be retained for that block.

Tenenhaus, A., & Tenenhaus, M. (2011). Regularized generalized canonical correlation analysis. Psychometrika, 76(2), 257-284.

Smilde, A.K., Mage, I., Naes, T., Hankemeier, T., Lips, M.A., Kiers, H.A., Acar, E., & Bro, R. (2016). Common and distinct components in data fusion. arXiv preprint arXiv:1607.02328.

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