rcontrib: Computes a measure of how correlated each variable in a set...

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

Computes a measure of how correlated each variable in a set is with the other variable, conditional on a nominated subset of them

Description

A measure of how correlated a variable is with those in a set is given by the square root of the sum of squares of the correlation coefficients between the variables and the other variables in the set (Cumming and Wooff, 2007). Here, the partial correlation between the subset of the variables listed in response that are not listed in include is calculated from the partial correlation matrix for the subset, adjusting for those variables in include. This is useful for manually deciding which of the variables not in include should next be added to it.

Usage

rcontrib(obj, ...)

Arguments

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

Details

rcontrib is the generic function for the rcontrib method. Use methods("rcontrib") to get all the methods for the rcontrib generic.

rcontrib.data.frame is a method for a data.frame.

rcontrib.matrix is a method for a matrix.

Value

A numeric giving the correlation measures.

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, intervalPVA

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