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Numero

Overview

In textbook examples, multivariable datasets are clustered into distinct subgroups that can be clearly identified by a set of optimal mathematical criteria. However, many real-world datasets arise from synergistic consequences of multiple effects, noisy and partly redundant measurements, and may represent a continuous spectrum of the different phases of a phenomenon. In medicine, complex diseases associated with ageing are typical examples. We postulate that population-based biomedical datasets (and many other real-world examples) do not contain an intrinsic clustered structure that would give rise to mathematically well-defined subgroups. From a modeling point of view, the lack of intrinsic structure means that the data points inhabit a contiguous cloud in high-dimensional space without abrupt changes in density to indicate subgroup boundaries, hence a mathematical criteria cannot segment the cloud reliably by its internal structure. Yet we need data-driven classification and subgrouping to aid decision-making and to facilitate the development of testable hypotheses. For this reason, we developed the Numero package, a more flexible and transparent process that allows human observers to create usable multivariable subgroups even when conventional clustering frameworks struggle.

Installation

# Install Numero from the CRAN repository:
install.packages("Numero")

Usage

The vignette of the package contains a practical real-life example of how to use the Numero R functions to define subgroups within a biomedical dataset.

library(Numero)
browseVignettes(package = "Numero")

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Version

Install

install.packages('Numero')

Monthly Downloads

318

Version

1.2.0

License

GPL (>= 2)

Maintainer

Ville-Petteri Makinen

Last Published

June 12th, 2019

Functions in Numero (1.2.0)

nroPreprocess

Data cleaning and standardization
nroTrain

Train self-organizing map
numero.clean

Clean datasets
nroPermute

Permutation analysis of map layout
nroPlot

Plot a self-organizing map
numero.summary

Summarize subgroup statistics
nroPostprocess

Standardization using existing parameters
numero.evaluate

Self-organizing map statistics
numero.create

Create a self-organizing map
numero.quality

Self-organizing map statistics
numero.subgroup

Interactive subgroup assignment
nroSummary

Estimate subgroup statistics
numero.plot

Plot results from SOM analysis
nroRcppMatrix

Safety check for Rcpp calls
numero.prepare

Prepare datasets for analysis
nroPrune

Reduce collinearity within a dataset
nroLabel

Label pruning
nroDestratify

Mitigate data stratification
nroPair

Match similar rows
nroKmeans

K-means clustering
nroAggregate

Regional averages on a self-organizing map
nroMatch

Best-matching districts
nroKohonen

Self-organizing map
nroImpute

Impute missing values
nroColorize

Assign colors based on value