casc: Covariate Assisted Spectral Clustering

Description Usage Arguments Value

View source: R/casc.R

Description

Covariate Assisted Spectral Clustering

Usage

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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, 2017, 4:53 p.m.