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

`var_exp`

is used to compute the proportion of the fraction of variance explained by a principal component analysis.

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`data` |
a data frame that contains the variables to be used in CUR decomposition. |

`standardize` |
logical. If |

`...` |
Additional arguments to be passed to |

The objective of CUR decomposition is to find the most relevant variables and observations within a data matrix and to reduce the dimensionality. It is well known that as more columns (variables) and rows are selected, the relative error will be lower; however, this is not true for k (number of components to calculate leverages). Given the above, this function seeks to find the best-balanced scenario of k, the number of relevant columns, and rows that have an error very close to the minimum, and that, in turn, uses a smaller amount of information.

`var_exp` |
a data frame with the proportion of explained variance for each principal component. |

Cesar Gamboa-Sanabria, Stefany Matarrita-Munoz, Katherine Barquero-Mejias, Greibin Villegas-Barahona, Mercedes Sanchez-Barba and Maria Purificacion Galindo-Villardon.

Mahoney697dCUR \insertRefvillegas2018modelodCUR \insertRefdynamyCURdCUR

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