casc: Covariate Assisted Spectral Clustering

In norbertbin/rCASC: Covariate assisted spectral clustering package

Description

Covariate Assisted Spectral Clustering

Usage

casc(adjMat, covMat, nBlocks, nPoints = 100, method = "regLaplacian",
  rowNorm = F, enhancedTuning = F, center = F, verbose = F,
  assortative = F, randStarts = 10, epsilon = 0.05)

Arguments

adjMat	An adjacency matrix
covMat	A covariate matrix
nBlocks	The number of clusters
nPoints	Number of iterations to find the optimal tuning parameter.
method	The form of the adjacency matrix to be used.
rowNorm	True if row normalization should be done before running kmeans.
enhancedTuning	If true, then the enhanced tuning procedure is used.
center	A boolean indicating if the covariate matrix columns should be centered.
verbose	A boolean indicating if casc output should include eigendecomposition.
assortative	A boolean indicating if the assortative version of casc should be used.
randStarts	Number of random restarts for kmeans.
epsilon	A threshold for identifying subspace discontinuities.

Value

A list with node cluster assignments, the the value of the tuning parameter used, the within cluster sum of squares, and the eigengap.

norbertbin/rCASC documentation built on May 23, 2019, 9:33 p.m.