boclust (version 0.1.1)

BossaSimi: Bossa Similarity

Description

Calculate the similarity matrix of Bossa scores which are obtained by boosting on single attribute.

Usage

BossaSimi(data, is.pca = TRUE, pca.sum.prop = 0.95, fix.pca.comp = FALSE,
  n.comp = 50, alpha = 1, pro.show = FALSE)

Arguments

data

A data.frame or matrix(n*p) of original categorical data.

is.pca

A logical variable indicating if the Bossa scores should transformed to principle components and then calculate the similarity matrix. It is recommended when processing the ultra-dimension data.

pca.sum.prop

A numeric indicating how many components should be reserved in order to make this proportion of variance. The default is pca.sum.prop = 0.95.

fix.pca.comp

A numeric variable indicating whether choosing the fixed number of components or the fixed proportion of variance and the default is to choose fixed proportion.

n.comp

The number of components of PCA. The default is n.comp = 50.

alpha

A power scaling for Bossa scores, representing the weight of variable sigma value.

pro.show

A logical indicator whether show the details of the process.

Value

An object including Bossa scores, Bossa dissimilarity and Bossa similarity(for OverlapClust.

Examples

# NOT RUN {
{
## generate sparse data from the toy model of CIDR
sparse.data <- data.frame(g.1 = c(0, 5, 0, 6, 8, 6, 7, 7), g.2 = c(5, 0, 0, 0, 5, 7, 5, 7))

## with low-dimensional data, pca is uncessary
bossa.change <- BossaSimi(sparse.data, is.pca = FALSE)

## data after normalization
data.after <- bossa.change$U.score.non.pca

## similarity matrix of normalized data
data.simi <- bossa.change$bossa.simi
}
# }