PVA: Selects a subset of variables using Principal Variable...

In growthPheno: Functional Analysis of Phenotypic Growth Data to Smooth and Extract Traits

Selects a subset of variables using Principal Variable Analysis (PVA)

Description

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.

Usage

PVA(obj, ...)

Arguments

obj	A data.frame containing the columns of variables from which the selection is to be made.
...	allows passing of arguments to other functions

Details

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.

Value

A data.frame giving the results of the variable selection. It will contain the columns Variable, Selected, h.partial, Added.Propn and Cumulative.Propn.

Author(s)

Chris Brien

References

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

See Also

PVA.data.frame, PVA.matrix, intervalPVA, rcontrib

growthPheno documentation built on Sept. 11, 2024, 6:42 p.m.