RobCoP: A Matlab Package for Robust CoPlot Analysis

Abstract

The graphical representation method, Robust CoPlot, is a robust variant of the classical CoPlot method. CoPlot is an adaptation of multidimensional scaling (MDS), and is a practical tool for visual inspection and rich interpretation of multivariate data. CoPlot enables presentation of a multidimensional dataset in a two dimensions, in a manner that relations between both variables and observations to be analyzed together. It has also been used as a supplemental tool to cluster analysis, data envelopment analysis (DEA) and outlier detection methods in the literature. However, this method is very sensitive to outliers. When a multidimensional dataset contains outliers, this can lead to undesirable consequences such as the inaccurate representation of the variables. The motivation is to produce Robust CoPlot that is not unduly affected by outliers. In this study, we have presented a new MATLAB package RobCoP for generating robust graphical representation of a multidimensional dataset. This study serves a useful purpose for researchers studying the implementation of Robust CoPlot method by providing a description of the software package RobCoP; it also offers some limited information on the Robust CoPlot analysis itself. The package presented here has enough flexibility to allow a user to select an MDS type and vector correlation method to produce either classical or Robust CoPlot results.

Evaluating the Efficiency of Bike-Sharing Stations with Data Envelopment Analysis

“…Since DEA is sensitive to outliers [60] and CoPlot has been often used as a supplemental tool to cluster analysis, DEA and outlier detection methods in the literature [61][62][63], we decided to suggest its application to the proposed analysis [64][65][66]. Additionally, this analysis allows reducing the number of variables/DMUs to obtain a sufficient differentiation between the efficiency scores, while following the rule of Dyson et al [27].…”

Visual research on the trustability of classical variable selection methods in Cox regression

“…Among a wide spectrum of graphical techniques which are available for the management of multidimensional dataset, coplot method has attracted much more attention for various purposes from a wide range of areas in recent years [3]. Studies that focus on robust coplot -an approach developed for reducing the impact of outliers -and a more flexible software, called RobCoP, which can produce coplot and robust coplot graphs are also available in the literature [4]. Robust coplot method is specifically convenient for visualizing and interpreting clinical data set.…”