Performs Principal Component Analysis (PCA) of the mean matrix aggregated over mode number indicated by `aggregmode`

.

1 | ```
pcamean(X, n, m, p, laba, labb, labc)
``` |

`X` |
Matrix (or data.frame coerced to a matrix) of order ( |

`n` |
Number of |

`m` |
Number of |

`p` |
Number of |

`laba` |
Optional vector of length |

`labb` |
Optional vector of length |

`labc` |
Optional vector of length |

A list including the following components:

`Y` |
An object of class |

`ev` |
A vector containing the eigenvalues of |

`A1` |
Component matrix for the |

`B1` |
Component matrix for the |

`C1` |
Component matrix for the |

`A2` |
Component matrix for the |

`B2` |
Component matrix for the |

`C2` |
Component matrix for the |

`aggregmode`

denotes the mode over which means are computed (1 for `A`

-mode, 2 for `B`

-mode, 3 for `C`

-mode).

`aggregmode`

is provided interactively.

Maria Antonietta Del Ferraro mariaantonietta.delferraro@yahoo.it

Henk A.L. Kiers h.a.l.kiers@rug.nl

Paolo Giordani paolo.giordani@uniroma1.it

H. Kaiser (1958). The varimax criterion for analytic rotation in factor analysis. *Psychometrika 23:187–200*.

C. Harris \& H. Kaiser (1964). Some mathematical notes on three-mode factor analysis. *Psychometrika 29:347–362*.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
data(TV)
TVdata=TV[[1]]
labSCALE=TV[[2]]
labPROGRAM=TV[[3]]
labSTUDENT=TV[[4]]
# permutation of the modes so that the A-mode refers to students
TVdata <- permnew(TVdata, 16, 15, 30)
TVdata <- permnew(TVdata, 15, 30, 16)
## Not run:
# PCA on the mean matrix
TVpcamean <- pcamean(TVdata, 30, 16, 15, labSTUDENT, labSCALE, labPROGRAM)
# PCA on the mean matrix (when labels are not available)
TVpcamean <- pcamean(TVdata, 30, 16, 15)
## End(Not run)
``` |

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