BKPC (version 1.0.1)

marginalRelevance: Marginal Relevance

Description

Calculates Marginal Relevance of each feature (variable) useful for class (group) separation. The marginal relevance score is a ratio of the between-group to within-group sum of squares.

Usage

marginalRelevance(x, y)

Arguments

x

a data matrix.

y

a response vector. Should be a factor.

Value

An object of class "marginalRelevance" including:

score

Marginal relevance score of each feature.

rank

The ranking in order of highest marginal relevance for each feature.

orderedData

Data matrix with columns reordered by the marginal relevance of the features.

bestVars

Features ordered by the marginal relevance.

References

Dudoit S., J. Fridlyand, T. P. Speed: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, 2002, Volume 97 No 457, pp 77-87.

See Also

plot.marginalRelevance microarray

Examples

# NOT RUN {

data(microarray)

profiles <- as.matrix(microarray[, -2309])
tumourType <-  microarray[, 2309]
 
 margRelv <- marginalRelevance(profiles, tumourType)

# plot 30 gene profiles with highest marginal relevance score
 plot(margRelv, type = "parallelcoord", n.feat = 50, col = tumourType )

 
# }
# NOT RUN {
# another example: wine data from gclus
library(gclus)
data(wine)
dt <- as.matrix(wine[, -1])
colnames(dt) <- names(wine[, -1])

label <- as.factor(wine[, 1])


margRelv <- marginalRelevance(dt, label)

#variables in order of their MR score
colnames(dt)[ margRelv$bestVars]


cparcoord(dt, order = margRelv$bestVars, col = label)
cpairs(dt, order = margRelv$bestVars, col = label)

# }