Multivariate regression trees for analysis of abundance data

被引:96
|
作者
Larsen, DR [1 ]
Speckman, PL
机构
[1] Univ Missouri, Dept Forestry, Columbia, MO 65211 USA
[2] Univ Missouri, Dept Stat, Columbia, MO 65211 USA
关键词
cluster analysis; multivariate regression; regression trees;
D O I
10.1111/j.0006-341X.2004.00202.x
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Multivariate regression tree methodology is developed and illustrated in a study predicting the abundance of several cooccurring plant species in Missouri Ozark forests. The technique is a variation of the approach of Segal (1992) for longitudinal data. It has the potential to be applied to many different types of problems in which analysts want to predict the simultaneous cooccurrence of several dependent variables. Multivariate regression trees can also be used as an alternative to cluster analysis in situations where clusters are defined by a set of independent variables and the researcher wants clusters as homogeneous as possible with respect to a group of dependent variables.
引用
收藏
页码:543 / 549
页数:7
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