Robust Coplot Analysis

Atilgan, Yasemin

doi:10.1080/03610918.2013.875571

Cited by 7 publications

(8 citation statements)

References 11 publications

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“…When the data contain outliers, obtained results from robust coplot are unaffected by these outliers. Robust coplot output is mainly generated with three steps [3].…”

Section: Robust Coplot Methodsmentioning

confidence: 99%

“…Many multivariate statistical methods analyze the observations and the variables separately. In robust coplot method, clusters of variables, clusters of observations and the characterization of observations can be seen in one graph [3]. 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].…”

Section: Introductionmentioning

confidence: 99%

“…In robust coplot method, clusters of variables, clusters of observations and the characterization of observations can be seen in one graph [3]. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Tutkun

Atilgan

2020

Hacettepe Journal of Mathematics and Statistics

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Multivariate models such as the Cox regression model, if developed carefully, are powerful tools for making prognostic prediction which are frequently used in studies of clinical outcomes. Many applications require a large number of variables to be modelled by using a relatively small patient sample. Determination of the important variables in a model is critical to understand the behaviour of phenomena as the independent variables contribute the most to the outcome. From a practical perspective, a small subset of independent variables are usually selected from a large data set without the loss of any predictive efficiency. Automatic variable selection algorithms in scientific studies are commonly used for obtaining interpretable and practically applicable models. However, the careless use of these methods may lead to statistical problems. The performance of the generated models may be poor due to the violation of assumption, omission of the important variables, problems of overfitting, and the problem of multicollinearity and outliers. In order to enhance the accuracy of a model, it is essential to explore the data and its main characteristics before making any statistical inference. This study suggests an approach for acquiring a trustworthy model selection procedure for survival data by performing classical variables selection methods, accompanied by a graphical visualization method, namely robust coplot. Thus, it enables us to investigate the discrimination of observations, clusters of the variables and clusters of the observations that are highly characterized by a particular variable in a one graph. We present an application of combined method, as an integral part of statistical modelling, on survival data on multiple myeloma to show how coplot results are used in automatic variable selection algorithm in Cox regression model-building.

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“…When the data contain outliers, obtained results from robust coplot are unaffected by these outliers. Robust coplot output is mainly generated with three steps [3].…”

Section: Robust Coplot Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tutkun

Atilgan

2020

Hacettepe Journal of Mathematics and Statistics

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“…Only the InStrct.MDSMethod field of the input structure is changed to a "RMDS" value, and since robust MDS is selected, the InStrct.OutlierRatio value should be given. The outlier ratio for the example is assumed to be 10% [13]. In addition, the output structure also contains an OutStrct.OutlierMatrix field to show which distances are taken as outliers during RMDS analysis.…”

Section: Nmds and Rmds Analysismentioning

confidence: 99%

“…If the dataset contains outliers, the representation of the variables may deviate strongly from those obtained from the clean data in CoPlot method. Aim of Robust CoPlot method is to reduce impact of outliers and try to fit the bulk of the data [13].…”

mentioning

confidence: 99%

RobCoP: A Matlab Package for Robust CoPlot Analysis

Atilgan¹,

Atilgan²

2017

OJS

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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.

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