Forward selection of explanatory variables
Forward selection of explanatory variables
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DOI：
10.1890/07-0986.1
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发表时间：
2008-09-01
期刊：
影响因子：
4.8
通讯作者：
Borcard, Daniel
中科院分区：
文献类型：
--
作者：
Blanchet, F. Guillaume;Legendre, Pierre;Borcard, Daniel
本文提出了在回归分析或规范冗余分析中使用前向选择解释变量的新方法。经典的正向选择方法存在两个问题：高度膨胀的I型误差和对解释方差量的高估。纠正这些问题将大大提高这种非常有用的生态建模方法的性能。为了防止第一个问题，我们提出一个两步法。首先，使用所有解释变量进行全局测试。当且仅当全局检验显著时，可以进行正向选择。为了防止被解释方差的高估，必须使用两个停止标准进行前向选择：(1)通常的alpha显著性水平和(2)使用所有解释变量计算的调整后的多重决定系数（R-a(2)）。当前向选择识别出使一个或另一个标准超过固定阈值的变量时，将拒绝该变量，并停止该过程。通过单变量和多变量响应数据的仿真验证了该方法的有效性。以美国犹他州布莱斯峡谷国家公园的数据为例，给出了一个生态的例子。
This paper proposes a new way of using forward selection of explanatory variables in regression or canonical redundancy analysis. The classical forward selection method presents two problems: a highly inflated Type I error and an overestimation of the amount of explained variance. Correcting these problems will greatly improve the performance of this very useful method in ecological modeling. To prevent the first problem, we propose a two-step procedure. First, a global test using all explanatory variables is carried out. If, and only if, the global test is significant, one can proceed with forward selection. To prevent overestimation of the explained variance, the forward selection has to be carried out with two stopping criteria: (1) the usual alpha significance level and (2) the adjusted coefficient of multiple determination (R-a(2)) calculated using all explanatory variables. When forward selection identifies a variable that brings one or the other criterion over the fixed threshold, that variable is rejected, and the procedure is stopped. This improved method is validated by simulations involving univariate and multivariate response data. An ecological example is presented using data from the Bryce Canyon National Park, Utah, USA.