biosensors.usc (version 1.0)

clustering: clustering

Description

Performs energy clustering with Wasserstein distance using quantile distributional representations as covariates.

Usage

clustering(data, clusters=3, iter_max=10, restarts=1)

Arguments

data

A biosensor object.

clusters

Number of clusters.

iter_max

Maximum number of iterations.

restarts

Number of restarts.

Value

An object of class bclustering: data A data frame with biosensor raw data. result A kgroups object (see energy library).

Examples

# NOT RUN {
# Data extracted from the paper: Hall, H., Perelman, D., Breschi, A., Limcaoco, P., Kellogg, R.,
# McLaughlin, T., Snyder, M., Glucotypes reveal new patterns of glucose dysregulation, PLoS
# biology 16(7), 2018.
file1 = system.file("extdata", "data_1.csv", package = "biosensors.usc")
file2 = system.file("extdata", "variables_1.csv", package = "biosensors.usc")
data = load_data(file1, file2)
clus = clustering(data, clusters=3)
# }