Review of Three Latent Class Cluster Analysis Packages: Latent Gold, poLCA, and MCLUST

被引：98

作者：

Haughton, Dominique ^{[1
]}

Legrand, Pascal ^{[1
,2
]}

Woolford, Sam ^{[1
]}

机构：

[1] Bentley Univ, DART, Waltham, MA 02452 USA

[2] CRCGM, ESC Clermont, DART Grp, Clermont Ferrand, France

来源：

AMERICAN STATISTICIAN | 2009年 / 63卷 / 01期

关键词：

Latent class models; Latent Gold (R); MCLUST; Mixture models; poLCA;

D O I：

10.1198/tast.2009.0016

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This article reviews three software packages that can be used to perform latent class cluster analysis, namely, Latent Gold (R), MCLUST, and poLCA. Latent Gold (R) is a product of Statistical Innovations whereas MCLUST and poLCA are packages written in R and are available through the web site http:// wwwr.r-project.org. We use a single dataset and apply each software package to develop a latent class cluster analysis for the data. This allows us to compare the features and the resulting clusters from each software package. Each software package has its strengths and weaknesses and we compare the software from the perspectives of usability, cost, data characteristics, and performance. Whereas each software package Utilizes the same methodology, we show that each results in a different cluster solution and suggest some rationales for deciding which package to Use.

引用

页码：81 / 91

页数：11

共 17 条

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