A Structural Equation Multidimensional Scaling Model for One-Mode Asymmetric Dissimilarity Data

Vera, José Ramón Navarro; Rivera, Chiristian D.

doi:10.1080/10705511.2014.856696

Cited by 5 publications

(2 citation statements)

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“…Stability (or sensitivity) analysis is an important topic in any area of data analysis, and although it has been addressed in MDS, it has received little attention in the corresponding applications. In most MDS applications, only one symmetric dissimilarity matrix is available and no probabilistic hypotheses are formulated (see Vera & Rivera, 2014, for a recent overview of MDS for asymmetric dissimilarity data).…”

Section: Introductionmentioning

confidence: 99%

Distance stability analysis in multidimensional scaling using the jackknife method

Vera

2016

Brit J Math & Statis

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Stability or sensitivity analysis is an important topic in data analysis that has received little attention in the application of multidimensional scaling (MDS), for which the only available approaches are given in terms of a coordinate-based analytical jackknife methodology. Although in MDS the prime interest is in assessing the stability of the points in the configuration, this methodology may be influenced by imprecisions resulting from the inherently necessary Procrustes method. This paper proposes an analytical distance-based jackknife procedure to study stability and cross-validation in MDS in terms of the jackknife distances, which is not influenced by the Procrustes method. For each object, the corresponding jackknife estimated points are considered as naturally clustered points, and stability and cross-validation are analysed in terms of the MDS distances arising from the jackknife procedure, on the basis of a weighted cluster-MDS algorithm. A jackknife-relevant configuration is also proposed for cross-validation in terms of coordinates, in a cluster-MDS framework.

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Section: Introductionmentioning

confidence: 99%

Distance stability analysis in multidimensional scaling using the jackknife method

Vera

2016

Brit J Math & Statis

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“…Other approaches to scale asymmetric data implemented in R are the following. Vera and Rivera (2014) embed MDS into a structural equation modeling framework. Their approach is implemented in the semds package (Vera and Mair 2019).…”

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More on Multidimensional Scaling and Unfolding in R: smacof Version 2

2022

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The smacof package offers a comprehensive implementation of multidimensional scaling (MDS) techniques in R. Since its first publication (De Leeuw and Mair 2009b) the functionality of the package has been enhanced, and several additional methods, features and utilities were added. Major updates include a complete re-implementation of multidimensional unfolding allowing for monotone dissimilarity transformations, including row-conditional, circular, and external unfolding. Additionally, the constrained MDS implementation was extended in terms of optimal scaling of the external variables. Further package additions include various tools and functions for goodness-of-fit assessment, unidimensional scaling, gravity MDS, asymmetric MDS, Procrustes, and MDS biplots. All these new package functionalities are illustrated using a variety of real-life applications.

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Cluster Distance-Based Regression

Vera

Val

2020

Studies in Classification, Data Analysis, and Knowledge Organization

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A Structural Equation Multidimensional Scaling Model for One-Mode Asymmetric Dissimilarity Data

Cited by 5 publications

References 20 publications

Distance stability analysis in multidimensional scaling using the jackknife method

Distance stability analysis in multidimensional scaling using the jackknife method

More on Multidimensional Scaling and Unfolding in R: smacof Version 2

Cluster Distance-Based Regression

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