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.
|Author||Jason T. Serviss and Jesper R. Gadin|
|Date of publication||None|
|Maintainer||Jason T Serviss <email@example.com>|
classify: Classification of the one dimensional points in a Pcp or Mlp...
ClusterSignificance-package: The ClusterSignificance package provides tools to assess if...
mlp: Projection of points into one dimension.
mlpMatrix: Simulated data used to demonstrate the Mlp method.
pcp: Projection of points into one dimension.
pcpMatrix: Simulated data used to demonstrate the Pcp method.
permute: Permutation test