Oblique Promax Rotation Applied to the Solutions in Multiple Correspondence Analysis

被引:7
|
作者
Kohei Adachi
机构
[1] Ritsumeikan University,Department of psychology
关键词
Multiple correspondence analysis; oblique rotation; promax method; categor-ical data;
D O I
10.2333/bhmk.31.1
中图分类号
学科分类号
摘要
There are many approaches to formulate multiple correspondence analysis of multi-item categorical data. A lower-rank approximation approach gives the freedom for the oblique rotation of axes. In the current paper, we apply the oblique rotation of axes to a variable-by-dimension matrix to arrive at simple structure. This matrix is defined in either of two different manners, that is, by treating categories as variables or, alternatively, by treating items as variables. For each of these two options, the standardized inner products between dimensions and variables are used as the elements of the component structure matrix. We adopt a promax method for the oblique rotation. In this method, scores are rotated in such a way that the above matrix is matched with the target matrix obtained from the result of the varimax rotation.
引用
下载
收藏
页码:1 / 12
页数:11
相关论文
共 50 条
  • [31] CORRESPONDENCE-ANALYSIS APPLIED TO GROUPED COHORT DATA
    GRASSI, M
    VISENTIN, S
    STATISTICS IN MEDICINE, 1994, 13 (23-24) : 2407 - 2425
  • [32] Number of Solutions for Motion and Structure from Multiple Frame Correspondence
    Robert J. Holt
    Arun N. Netravali
    International Journal of Computer Vision, 1997, 23 : 5 - 15
  • [33] Number of solutions for motion and structure from multiple frame correspondence
    Holt, RJ
    Netravali, AN
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 1997, 23 (01) : 5 - 15
  • [34] Analysis of arthritis data through multiple correspondence analysis
    Sathyanarayana, M.
    Haragopal, V. V.
    Pandit, S. N. N.
    Prathibha, N.
    Padmini, S.
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES, 2007, 3 (02): : 395 - 402
  • [35] MIMCA: multiple imputation for categorical variables with multiple correspondence analysis
    Audigier, Vincent
    Husson, Francois
    Josse, Julie
    STATISTICS AND COMPUTING, 2017, 27 (02) : 501 - 518
  • [36] MIMCA: multiple imputation for categorical variables with multiple correspondence analysis
    Vincent Audigier
    François Husson
    Julie Josse
    Statistics and Computing, 2017, 27 : 501 - 518
  • [37] Multiple correspondence analysis in S-PLUS
    Ambrogi, F
    Biganzoli, E
    Boracchi, P
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2005, 79 (02) : 161 - 167
  • [38] A Multiple Correspondence Analysis to Organize Data Cubes
    Ben Messaoud, Riadh
    Boussaid, Omar
    Rabaseda, Sabine Loudcher
    DATABASES AND INFORMATION SYSTEMS IV, 2007, 155 : 133 - 146
  • [39] EQUALITY CONSTRAINTS IN MULTIPLE CORRESPONDENCE-ANALYSIS
    VANBUUREN, S
    DELEEUW, J
    MULTIVARIATE BEHAVIORAL RESEARCH, 1992, 27 (04) : 567 - 583
  • [40] MULTIPLE CORRESPONDENCE-ANALYSIS AND NEIGHBORING RELATION
    ESCOFIER, B
    DATA ANALYSIS, LEARNING SYMBOLIC AND NUMERIC KNOWLEDGE, 1989, : 55 - 62