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A Multivariate Logistic Distance Model for the Analysis of Multiple Binary Responses

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Listed:
  • Hailemichael M. Worku

    (Leiden University)

  • Mark Rooij

    (Leiden University)

Abstract

We propose a Multivariate Logistic Distance (MLD) model for the analysis of multiple binary responses in the presence of predictors. The MLD model can be used to simultaneously assess the dimensional/factorial structure of the data and to study the effect of the predictor variables on each of the response variables. To enhance interpretation, the results of the proposed model can be graphically represented in a biplot, showing predictor variable axes, the categories of the response variables and the subjects’ positions. The interpretation of the biplot uses a distance rule. The MLD model belongs to the family of marginal models for multivariate responses, as opposed to latent variable models and conditionally specified models. By setting the distance between the two categories of every response variable to be equal, the MLD model becomes equivalent to a marginal model for multivariate binary data estimated using a GEE method. In that case the MLD model can be fitted using existing statistical packages with a GEE procedure, e.g., the genmod procedure from SAS or the geepack package from R. Without the equality constraint, the MLD model is a general model which can be fitted by its own right. We applied the proposed model to empirical data to illustrate its advantages.

Suggested Citation

  • Hailemichael M. Worku & Mark Rooij, 2018. "A Multivariate Logistic Distance Model for the Analysis of Multiple Binary Responses," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 124-146, April.
    • Handle: RePEc:spr:jclass:v:35:y:2018:i:1:d:10.1007_s00357-018-9251-4
    DOI: 10.1007/s00357-018-9251-4
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    References listed on IDEAS

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    1. Andreas Ziegler & Gerhard Arminger, 1995. "Analyzing the Employment Status with Panel Data from the GSOEP: A Comparison of the MECOSA and the GEE1 Approach for Marginal Models," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 64(1), pages 72-80.
    2. Mark Rooij, 2009. "Ideal Point Discriminant Analysis Revisited with a Special Emphasis on Visualization," Psychometrika, Springer;The Psychometric Society, vol. 74(2), pages 317-330, June.
    3. Cheng, Guang & Yu, Zhuqing & Huang, Jianhua Z., 2013. "The cluster bootstrap consistency in generalized estimating equations," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 33-47.
    4. Wei, Xin, 2012. "%PROC_R: A SAS Macro that Enables Native R Programming in the Base SAS Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 46(c02).
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