Description Usage Arguments Details Value Examples

Determination of the number of components based on cross-validated method or Bayesian information criterion (BIC)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
ncompsearch(
X,
Y = NULL,
Z = NULL,
comps = 1:3,
lambdaX = NULL,
lambdaY = NULL,
lambdaXsup = NULL,
lambdaYsup = NULL,
eta = 1,
type = "lasso",
inX = NULL,
inY = NULL,
inXsup = NULL,
inYsup = NULL,
muX = 0,
muY = 0,
nfold = 5,
regpara = FALSE,
maxrep = 3,
minpct = 0,
maxpct = 1,
criterion = c("CV", "BIC")[1],
whichselect = NULL,
intseed = 1
)
## S3 method for class 'ncompsearch'
print(x, ...)
## S3 method for class 'ncompsearch'
plot(x, ...)
``` |

`X` |
a matrix or list of matrices indicating the explanatory variable(s). This parameter is required. |

`Y` |
a matrix or list of matrices indicating objective variable(s). This is optional. If there is no input for Y, then PCA is implemented. |

`Z` |
a vector, response variable(s) for implementing the supervised version of (multiblock) PCA or PLS. This is optional. The length of Z is the number of subjects. If there is no input for Z, then unsupervised PLS/PCA is implemented. |

`comps` |
numeric vector for the maximum numbers of componets to be considered. |

`lambdaX` |
numeric vector of regularized parameters for X, with a length equal to the number of blocks. If lambdaX is omitted, no regularization is conducted. |

`lambdaY` |
numeric vector of regularized parameters for Y, with a length equal to the number of blocks. If lambdaY is omitted, no regularization is conducted. |

`lambdaXsup` |
numeric vector of regularized parameters for the super weight of X with length equal to the number of blocks. If omitted, no regularization is conducted. |

`lambdaYsup` |
numeric vector of regularized parameters for the super weight of Y with length equal to the number of blocks. If omitted, no regularization is conducted. |

`eta` |
numeric scalar indicating the parameter indexing the penalty family. This version contains only choice 1. |

`type` |
a character, indicating the penalty family. In this version, only one choice is available: "lasso." |

`inX` |
a (list of) numeric vector to specify the variables of X which are always in the model. |

`inY` |
a (list of) numeric vector to specify the variables of X which are always in the model. |

`inXsup` |
a (list of) numeric vector to specify the blocks of X which are always in the model. |

`inYsup` |
a (list of) numeric vector to specify the blocks of Y which are always in the model. |

`muX` |
a numeric scalar for the weight of X for the supervised case. 0 <= muX <= 1. |

`muY` |
a numeric scalar for the weight of Y for the supervised case. 0 <= muY <= 1. |

`nfold` |
number of folds - default is 5. |

`regpara` |
logical, If TRUE, the regularized parameters search is also conducted simultaneously. |

`maxrep` |
numeric scalar for the number of iteration. |

`minpct` |
minimum candidate parameters defined as a percentile of automatically determined (possible) candidates. |

`maxpct` |
maximum candidate parameters defined as a percentile of automatically determined (possible) candidates. |

`criterion` |
a character, the evaluation criterion, "CV" for cross-validation, based on a matrix element-wise error, and "BIC" for Bayesian information criteria. The "BIC" is the default. |

`whichselect` |
which blocks selected. |

`intseed` |
seed number for the random number in the parameter estimation algorithm. |

`x` |
an object of class " |

`...` |
further arguments passed to or from other methods. |

This function searches for the optimal number of components.

`comps` |
numbers of components |

`mincriterion` |
minimum criterion values |

`criterions` |
criterion values |

`optncomp` |
optimal number of components based on minimum cross-validation error |

1 2 3 4 5 6 7 | ```
##### data #####
tmpdata = simdata(n = 50, rho = 0.8, Yps = c(10, 12, 15), Xps = 20, seed=1)
X = tmpdata$X; Y = tmpdata$Y
##### number of components search #####
ncomp1 = ncompsearch(X, Y, comps = c(1, 5, 10*(1:2)), nfold=5)
plot(ncomp1)
``` |

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