Description Usage Arguments Details Value Author(s) See Also

An internal function called by `mbpca`

.

1 |

`x` |
The input matrix, rows are observations, columns are variables |

`maxiter` |
Number of maximum interation the algorithm can run |

`kp` |
The number (>=1) or proportion (<1) of variables want to keep. It could be a single value or a vector has the same length as x so the sparsity of individual matrix could be different. |

`kt` |
The number (>=1) or proportion (<1) of non-zero scores for obvservations. |

`weight.p` |
The weight of variables. It could be 1) a vector has the same length as x, one value for each table/block; 2) one number, all variables share the same weight or 3) a list of vectors, the length of each vector should be the same with the columns numbers of the corresponding table/block, so every variables has a unique weight. |

`weight.t` |
The weight for observation. For accepted values or formats, see weight.p. |

`pos` |
Logical value, if only non-negaitve values in the loading and score vectors. |

`unit.pb` |
Logical value, whether the length of table/block loading should be unit length. |

`unit.tb` |
Logical value, whether the length of table/block score should be unit length. |

This function also use the NIPALS algorithm, but it generalized nipalsSoftK from several aspects: 1. Allowing sparsity on both columns and rows of matrices 2. Allowing weights for columns and rows 3. Allowing loading and/or score vectors of blocks to be unit length 4. Allowing only positive number in loading and score vectors

an `list`

object contains the following elements:

`tb`

- the block scores

`pb`

- the block loadings

`t`

- the global scores

`w`

- the wegihts of block scores to construct the global score.

Chen Meng

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