Bennett Bradley C, Husby Chad E
Department of Biological Sciences and Center for Natural Products, Florida International University, Miami, FL 33199, USA.
J Ethnopharmacol. 2008 Mar 28;116(3):422-30. doi: 10.1016/j.jep.2007.12.006. Epub 2007 Dec 23.
Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws.
We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador).
We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families.
Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels.
Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.
植物药典是植物区系的非随机子集,某些分类群的代表性过高或过低。莫尔曼[莫尔曼,D.E.,1979年。符号与选择性:对美洲原住民医学民族植物学的统计分析,《民族药理学杂志》1,111 - 119]引入线性回归/残差分析来研究这些模式。然而,常用的回归分析存在几个统计学缺陷。
我们使用列联表和二项式分析来研究舒阿尔人(来自厄瓜多尔亚马逊地区)药用植物的使用模式。
我们首先使用莫尔曼的方法分析舒阿尔人的数据,并对其进行修改以更好地满足线性回归分析的要求。其次,我们评估了用于拟合优度的精确随机化列联表检验。第三,我们开发了一个二项式模型来检验各个科中植物的非随机选择情况。
经过修改的回归模型(符合线性回归的假设)使R²从0.59降至0.38,但并未消除与回归分析相关的所有问题。列联表分析表明,整个植物区系偏离了所有科中药用植物比例均等的零模型。在二项式分析中,115个被子植物科中只有10个科与零模型有显著差异。这10个科在很大程度上导致了在更高分类水平上出现的模式。
列联表和二项式分析为回归方法提供了一种简单且统计有效的替代方法。