Package 'cord' reference manual

Title:	Community Estimation in G-Models via CORD
Description:	Partition data points (variables) into communities/clusters, similar to clustering algorithms, such as k-means and hierarchical clustering. This package implements a clustering algorithm based on a new metric CORD, defined for high dimensional parametric or semi-parametric distributions. Read http://arxiv.org/abs/1508.01939 for more details.
Authors:	Xi (Rossi) LUO, Florentina Bunea, Christophe Giraud
Maintainer:	Xi (Rossi) LUO <[email protected]>
License:	GPL-3
Version:	0.1.1
Built:	2024-10-24 03:42:59 UTC
Source:	https://github.com/cran/cord

Community estimation in G-models via CORD

Description

Partition data points (variables) into clusters/communities. Reference: Bunea, F., Giraud, C., & Luo, X. (2015). Community estimation in $G$ -models via CORD. arXiv preprint arXiv:1508.01939. http://arxiv.org/abs/1508.01939.

Usage

cord(X, tau = 2 * sqrt(log(ncol(X))/nrow(X)), kendall = T,
  input = c("data", "cor", "dist"))
cord(X, tau = 2 * sqrt(log(ncol(X))/nrow(X)), kendall = T,
  input = c("data", "cor", "dist"))

Arguments

`X`	Input data matrix. It should be an n (samples) by p (variables) matrix when `input` is set to the value "data" by default. It can also be a p by p symmetric matrix when `X` is a correlation matrix or a distance matrix if `input` is set accordingly.
`tau`	Threshold to use at each iteration. A theoretical choice is about $2n^{-1/2}\log^{1/2} p$ .
`kendall`	Whether to compute Kendall's tau correlation matrix from `X`, when `input` is set to "data". If `FALSE`, Pearson's correlation will be computed, usually faster for large p.
`input`	Type of input `X`. It should be set to "data" when `X` is an n (samples) by p (variables) matrix. If `X` is a correlation matrix or a distance matrix, it should be set to "cor" or "dist" respectively.

Value

list with one element: a vector of integers showing which cluster/community each point is assigned to.

Examples

set.seed(100)
X <- 2*matrix(rnorm(200*2), 200, 10)+matrix(rnorm(200*10), 200, 10)
cord(X)
set.seed(100)
X <- 2*matrix(rnorm(200*2), 200, 10)+matrix(rnorm(200*10), 200, 10)
cord(X)