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首页> 外文期刊>International Journal of Applied Mathematics & Statistics >On The Empirical Performance Of Non-Metric Multidimensional Scaling In Vegetation Studies
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On The Empirical Performance Of Non-Metric Multidimensional Scaling In Vegetation Studies

机译：非度量多维尺度在植被研究中的经验表现

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摘要

Non-metric multidimensional scaling (NMDS) is widely used as a routine method for ordination in vegetation studies. Its use in statistical softwares often requires the choice of several options on which the accuracy of results will depend. This study focuses on the combined effect of sample size, similarity/dissimilarity indexes, data standardization and structure of data matrix (abundance and binary) on NMDS efficiency based on real data from the Lama Forest Reserve in Southem-Benin. The Spearman's Rank Correlation coefficient and the s-stress were used as an assessment criterion. All the four factors were found to influence the efficiency of the NMDS and the samples (plots) standardization to equal totals gave the best results among standardization procedures considered. The Jaccard and Sorensen similarity/dissimilarity indexes performed equally whatever the nature of the matrix. However, with binary matrices, Sokal and Michener similarity index performed better. A quadratic relationship was noted between s-stress and sample size. A lower optimal sample size (75 plots) was observed for the binary matrices than for the abundance ones (90 plots).
机译：非度量多维缩放（NMDS）被广泛用作植被研究中的常规排序方法。在统计软件中使用它通常需要选择几个选项，这些选项将取决于结果的准确性。这项研究基于南贝宁的喇嘛森林保护区的真实数据，着重于样本量，相似/相异指标，数据标准化和数据矩阵结构（丰度和二元）对NMDS效率的综合影响。 Spearman的等级相关系数和s应力用作评估标准。发现所有这四个因素均会影响NMDS的效率，并且在考虑的标准化程序中，将样品（样点）标准化为相等的总量可得出最佳结果。无论矩阵的性质如何，Jaccard和Sorensen相似度/不相似度指标均表现相同。但是，使用二元矩阵时，Sokal和Michener相似指数表现更好。 s应力与样本量之间存在二次关系。对于二元矩阵，观察到的最优样本量（75个图）要比丰度样本（90个图）的最优样本量低。

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