ClusterSignificance

ClusterSignificance-package


It is often a common wish after clustering procedure to assess how
significant a seen separation is. This package investigates clusters of two
or more groups by projecting all points to a line or curve. The placement on
this line is used for classification of the points and the probability of 
the seen separation just being because of chance is evaluated using
a permutation method.


package

The ClusterSignificance package provides tools to assess
if clusters have a separation different from random or permuted
data. ClusterSignificance investigates clusters of two or more
groups by first, projecting all points onto a one dimensional
line. Cluster separations are then scored and the probability
of the seen separation being due to chance is evaluated using a
permutation method.

Jason T Serviss

Investigates Significance of Clusters by Reducing the Data to
One Dimension to be Able to Easy Set a Score for the
Separation, and a p-Value is then Calculated from Permutations
of the Original Data

ClusterSignificance-package function

It is often a common wish after clustering procedure to assess how
significant a seen separation is. This package investigates clusters of two
or more groups by projecting all points to a line or curve. The placement on
this line is used for classification of the points and the probability of 
the seen separation just being because of chance is evaluated using
a permutation method.

ClusterSignificance-package: A package with purpose of giving a p-value to separations of clusters.

Description

Arguments

Details

References