Description Usage Arguments Details Value Author(s) References See Also Examples

Estimate the number of dimensions for the Principal Component Analysis by cross-validation

1 2 3 4 |

`X` |
a data.frame with continuous variables; with missing entries or not |

`ncp.min` |
integer corresponding to the minimum number of components to test |

`ncp.max` |
integer corresponding to the maximum number of components to test |

`method` |
"Regularized" by default or "EM" |

`scale` |
boolean. TRUE implies a same weight for each variable |

`method.cv` |
string with the values "gcv" for generalised cross-validation, "loo" for leave-one-out or "Kfold" cross-validation |

`nbsim` |
number of simulations, useful only if method.cv="Kfold" |

`pNA` |
percentage of missing values added in the data set, useful only if method.cv="Kfold" |

`ind.sup` |
a vector indicating the indexes of the supplementary individuals |

`quanti.sup` |
a vector indicating the indexes of the quantitative supplementary variables |

`quali.sup` |
a vector indicating the indexes of the categorical supplementary variables |

`threshold` |
the threshold for assessing convergence |

`verbose` |
boolean. TRUE means that a progressbar is writtent |

For leave-one-out (loo) cross-validation, each cell of the data matrix is alternatively removed and predicted with a PCA model using ncp.min to ncp.max dimensions. The number of components which leads to the smallest mean square error of prediction (MSEP) is retained.
For the Kfold cross-validation, pNA percentage of missing values is inserted and predicted with a PCA model using ncp.min to ncp.max dimensions. This process is repeated nbsim times. The number of components which leads to the smallest MSEP is retained.

For both cross-validation methods, missing entries are predicted using the imputePCA function, it means using the regularized iterative PCA algorithm (method="Regularized") or the iterative PCA algorithm (method="EM"). The regularized version is more appropriate when there are already many missing values in the dataset to avoid overfitting issues.

Cross-validation (especially method.cv="loo") is time-consuming. The generalised cross-validation criterion (method.cv="gcv") can be seen as an approximation of the loo cross-validation criterion which provides a straightforward way to estimate the number of dimensions without resorting to a computationally intensive method.

This argument scale has to be chosen in agreement with the PCA that will be performed. If one wants to perform a normed PCA (where the variables are centered and scaled, i.e. divided by their standard deviation), then the argument scale has to be set to the value TRUE.

`ncp` |
the number of components retained for the PCA |

`criterion` |
the criterion (the MSEP) calculated for each number of components |

Francois Husson husson@agrocampus-ouest.fr and Julie Josse julie.josse@polytechnique.edu

Bro, R., Kjeldahl, K. Smilde, A. K. and Kiers, H. A. L. (2008) Cross-validation of component models: A critical look at current methods. Analytical and Bioanalytical Chemistry, 5, 1241-1251.

Josse, J. and Husson, F. (2011). Selecting the number of components in PCA using cross-validation approximations. Computational Statistics and Data Analysis. 56 (6), pp. 1869-1879.

1 2 3 4 5 | ```
## Not run:
data(orange)
nb <- estim_ncpPCA(orange,ncp.min=0,ncp.max=4)
## End(Not run)
``` |

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