Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables
Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables
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DOI：
10.1023/a:1016250716679
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发表时间：
2002-08-01
影响因子：
3.4
通讯作者：
Mac Nally, R
中科院分区：
文献类型：
--
作者：
Mac Nally, R
生态学家和保护生物学家经常使用多元回归（MR）试图确定影响响应变量的因素，如物种丰富度或发生率。许多常用的回归方法可能会产生虚假的结果，由于多重共线性。Mac Nally（2000，Biodiversity and Conservation 9：655-671）认为，实际上有两种MR建模：（1）寻找最佳预测模型;（2）隔离可归因于每个预测变量的方差量。前者已经吸引了大量的关注与标准（惩罚模型的复杂性-参数的数量的模型拟合的措施）和贝叶斯因子为基础的方法已经提出，而后者一直很少考虑，虽然分层方法似乎有前途（e。G.分层分区）。如果这两种方法在保留哪些预测变量上达成一致，那么更有可能找到有意义的预测变量（在所考虑的那些预测变量中）。有一个问题是，虽然分层划分允许根据独立解释能力的大小对预测变量进行排序，但没有（统计）方法来决定保留哪些变量。一个解决方案，使用随机化的数据矩阵加上分层分区，是一个生态的例子。
Ecologists and conservation biologists frequently use multiple regression (MR) to try to identify factors influencing response variables such as species richness or occurrence. Many frequently used regression methods may generate spurious results due to multicollinearity. Mac Nally (2000, Biodiversity and Conservation 9: 655-671) argued that there are actually two kinds of MR modelling: (1) seeking the best predictive model; and (2) isolating amounts of variance attributable to each predictor variable. The former has attracted most attention with a plethora of criteria (measures of model fit penalized for model complexity - number of parameters) and Bayes-factor-based methods having been proposed, while the latter has been little considered, although hierarchical methods seem promising (e. g. hierarchical partitioning). If the two approaches agree on which predictor variables to retain, then it is more likely that meaningful predictor variables (of those considered) have been found. There has been a problem in that, while hierarchical partitioning allowed the ranking of predictor variables by amounts of independent explanatory power, there was no (statistical) way to decide which variables to retain. A solution using randomization of the data matrix coupled with hierarchical partitioning is presented, as is an ecological example.