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

View source: R/S3methodsDeprecations.r

Principal Variable Analysis (PVA) (Cumming and Wooff, 2007) selects a subset from a set of the variables such that the variables in the subset are as uncorrelated as possible, in an effort to ensure that all aspects of the variation in the data are covered.

1 |

`obj` |
A |

`...` |
allows passing of arguments to other functions |

`PVA`

is the generic function for the `PVA`

method.
Use methods("PVA") to get all the methods for the PVA generic.

`PVA.data.frame`

is a method for a `data.frame`

.

`PVA.matrix`

is a method for a `matrix`

.

A `data.frame`

giving the results of the variable selection.
It will contain the columns `Variable`

, `Selected`

,
`h.partial`

, `Added.Propn`

and `Cumulative.Propn`

.

Chris Brien

Cumming, J. A. and D. A. Wooff (2007) Dimension reduction via principal variables. *Computational Statistics
and Data Analysis*, **52**, 550–565.

`PVA.data.frame`

, `PVA.matrix`

, `intervalPVA`

, `rcontrib`

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