Description Usage Arguments Value References Examples

Variable selection with Lasso and Group Lasso penalties to identify component and distinctive components. This algorithm incorporates a multi-start procedure to deal with the possible existence of local minima.

1 | ```
sparseSCA(DATA, Jk, R, LASSO, GROUPLASSO, MaxIter, NRSTARTS, method)
``` |

`DATA` |
A matrix, which contains the concatenated data with the same subjects from multiple blocks. |

`Jk` |
A vector containing number of variables in the concatinated data matrix. |

`R` |
Number of components (R>=2). |

`LASSO` |
A Lasso tuning parameter. |

`GROUPLASSO` |
A group Lasso tuning parameter. |

`MaxIter` |
The maximum rounds of iterations. It should be a positive integer. The default value is 400. |

`NRSTARTS` |
Multi-start procedure: The number of multi-starts. The default value is 20. |

`method` |
"datablock" or "component". If |

`Pmatrix` |
The best estimated component loading matrix (i.e., P), if multi-starts >= 2. |

`Tmatrix` |
The best estimated component score matrix (i.e., T), if multi-starts >= 2. |

`Lossvec` |
A list of vectors containing the loss in each iteration for each multi-start. |

Friedman, J., Hastie, T., & Tibshirani, R. (2010). A note on the group lasso and a sparse group lasso. arXiv preprint arXiv:1001.0736.

Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(1), 49-67.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## Not run:
DATA1 <- matrix(rnorm(50), nrow=5)
DATA2 <- matrix(rnorm(100), nrow=5)
DATA <- cbind(DATA1, DATA2)
Jk <- c(10, 20)
R <- 5
LASSO <- 0.2
GROUPLASSO <- 0.4
MaxIter <- 400
results <- sparseSCA(DATA, Jk, R, LASSO, GROUPLASSO,
MaxIter, NRSTARTS = 10, method = "datablock")
results$Pmatrix
## End(Not run)
``` |

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