ADPROCLUS：用于通过变量数据矩阵拟合对象加性轮廓聚类模型的图形用户界面。

ADPROCLUS: a graphical user interface for fitting additive profile clustering models to object by variable data matrices.

机构信息

Research Group of Quantitative Psychology and Individual Differences, Department of Psychology, Katholieke Universiteit Leuven, Tiensestraat 102, Box 3713, 3000, Leuven, Belgium.

出版信息

Behav Res Methods. 2011 Mar;43(1):56-65. doi: 10.3758/s13428-010-0033-0.

DOI:10.3758/s13428-010-0033-0

PMID:21287114

Abstract

In many areas of psychology, one is interested in disclosing the underlying structural mechanisms that generated an object by variable data set. Often, based on theoretical or empirical arguments, it may be expected that these underlying mechanisms imply that the objects are grouped into clusters that are allowed to overlap (i.e., an object may belong to more than one cluster). In such cases, analyzing the data with Mirkin's additive profile clustering model may be appropriate. In this model: (1) each object may belong to no, one or several clusters, (2) there is a specific variable profile associated with each cluster, and (3) the scores of the objects on the variables can be reconstructed by adding the cluster-specific variable profiles of the clusters the object in question belongs to. Until now, however, no software program has been publicly available to perform an additive profile clustering analysis. For this purpose, in this article, the ADPROCLUS program, steered by a graphical user interface, is presented. We further illustrate its use by means of the analysis of a patient by symptom data matrix.

摘要

在心理学的许多领域中，人们感兴趣的是揭示产生对象的潜在结构机制，这些机制是通过可变数据集生成的。通常，基于理论或经验论据，可以预期这些潜在机制意味着对象被分为允许重叠的聚类（即，一个对象可能属于多个聚类）。在这种情况下，使用 Mirkin 的加性剖面聚类模型分析数据可能是合适的。在这个模型中：（1）每个对象可以不属于任何聚类，也可以属于一个或多个聚类；（2）每个聚类都有一个特定的变量剖面；（3）对象在变量上的得分可以通过添加对象所属的聚类的特定变量剖面来重建。然而，到目前为止，还没有公开的软件程序可以执行加性剖面聚类分析。为此，本文提出了一个由图形用户界面引导的 ADPROCLUS 程序。我们进一步通过对症状数据矩阵的患者分析来说明其用法。