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

被引:40
作者
Mair, Patrick [1 ]
Groenen, Patrick J. F. [2 ]
de Leeuw, Jan [3 ]
机构
[1] Harvard Univ, Dept Psychol, 33 Kirkland St, Cambridge, MA 02138 USA
[2] Erasmus Univ, Rotterdam, Netherlands
[3] Univ Calif Los Angeles, Los Angeles, CA USA
来源
JOURNAL OF STATISTICAL SOFTWARE | 2022年 / 102卷 / 10期
关键词
multidimensional scaling; constrained multidimensional scaling; multidimensional unfolding; SMACOF; R; CONFIDENCE-REGIONS; DISSIMILARITY; JACKKNIFE; MODELS; FIT; SIMILARITY; GOODNESS; RANKINGS; DISTANCE; STIMULUS;
D O I
10.18637/jss.v102.i10
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:1 / 47
页数:47
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