loads: Variable Ranking

In KODAMA: Knowledge Discovery by Accuracy Maximization

Variable Ranking

Description

This function can be used to extract the variable ranking when KODAMA is performed with the PLS-DA classifier.

Usage

loads(model,method=c("loadings","kruskal.test"))

Arguments

model	output of KODAMA.
method	method to be used. Choices are "loadings" and "kruskal.test".

Value

The function returns a vector of values indicating the "importance" of each variable. If "method="loadings" the average of the loading of the first component of PLS models based on the cross-validated accuracy maximized vector is computed. If "method="kruskal.test" the average of minus logarithm of p-value of Kruskal-Wallis Rank Sum test is computed.

Author(s)

Stefano Cacciatore

References

Cacciatore S, Luchinat C, Tenori L
Knowledge discovery by accuracy maximization.
Proc Natl Acad Sci U S A 2014;111(14):5117-22. doi: 10.1073/pnas.1220873111. Link

Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA
KODAMA: an updated R package for knowledge discovery and data mining.
Bioinformatics 2017;33(4):621-623. doi: 10.1093/bioinformatics/btw705. Link

See Also

KODAMA.matrix,KODAMA.visualization

Examples

 data(iris)
 data=iris[,-5]
 labels=iris[,5]
 kk=KODAMA.matrix(data,FUN="PLS-DA")
 loads(kk)

KODAMA documentation built on Jan. 12, 2023, 5:08 p.m.