IntClust (version 0.1.0)

FeaturesOfCluster: List all features present in a selected cluster of objects

Description

The function FeaturesOfCluster lists the number of features objects of the cluster have in common. A threshold can be set selecting among how many objects of the cluster the features should be shared. An optional plot of the features is available.

Usage

FeaturesOfCluster(leadCpds, data, threshold = 1, plot = TRUE,
  plottype = "new", location = NULL)

Arguments

leadCpds

A character vector containing the objects one wants to investigate in terms of features.

data

The data matrix.

threshold

The number of objects the features at least should be shared amongst. Default is set to 1 implying that the features should be present in at least one of the objects specified in leadCpds.

plot

Logical. Indicates whether or not a BinFeaturesPlot should be set up for the selectcion of objects and discovered features. Default is FALSE.

plottype

Should be one of "pdf","new" or "sweave". If "pdf", a location should be provided in "location" and the figure is saved there. If "new" a new graphic device is opened and if "sweave", the figure is made compatible to appear in a sweave or knitr document, i.e. no new device is opened and the plot appears in the current device or document. Default is "new".

location

If plottype is "pdf", a location should be provided in "location" and the figure is saved there. Default is NULL.

Value

A plot indicating the values of the features of the LeadCpds in green and those of the others in blue. It list all features which are present in at least the threshold number of objects. By including all other objects as well, one can see whether features are common in the objects or rather specific for the cluster.

Further, it returns a list with 2 items. The first indicates the number of shared features among the objects. This provides an overview of which objects are more similar than others. The second item is a character vector of the plotted features such that these can be retrieved for further investigation.

Examples

# NOT RUN {
# }
# NOT RUN {
data(fingerprintMat)

Lead=rownames(fingerprintMat)[1:5]

FeaturesOfCluster(leadCpds=Lead,data=fingerprintMat,
threshold=1,plot=TRUE,plottype="new",location=NULL)
# }