Using between-model comparisons to fine-tune linear models of species ranges
Using between-model comparisons to fine-tune linear models of species ranges
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DOI：
10.1046/j.1365-2699.2000.00408.x
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发表时间：
2000-03-01
影响因子：
3.9
通讯作者：
Cumming, GS
中科院分区：
文献类型：
--
作者：
Cumming, GS
目的提供一种客观的方法来解决与逻辑回归模型的发展相关的一些问题：如何比较不同的模型，确定样本量的充分性，阳性细胞与阴性细胞的比例对模型准确性的影响，以及非--随机分布应进行测试。位置测试数据来自南部非洲。方法该方法主要依赖于使用AUC（曲线下面积）统计量，基于ROC（阈值受试者工作特征）图，用于模型间比较。使用弯曲蜱Amblyomma hebraeum Koch（阿卡里目：硬蜱科）的分布数据来说明这些方法。结果概述了估计最小样本量和更准确的假设检验的方法。如果评分为阳性的细胞样本量足够，则逻辑回归对于未采集细胞可评分为阴性的假设具有稳健性。研究了不同地区气温和降水的变化。hebraeum已收集到的是显着低于预期的随机抽样点在整个数据集，这表明，网站内的变化可能是一个重要的决定因素，其distribution.Main结论模型之间的比较依赖于AUC可以用来提高客观性的发展和完善的logistic回归模型。地点间和地点内的变异性都应被视为决定物种分布的潜在重要因素。
Aim To offer an objective approach to some of the problems associated with the development of logistic regression models: how to compare different models, determination of sample size adequacy, the influence of the ratio of positive to negative cells on model accuracy, and the appropriate scale at which the hypothesis of a non-random distribution should be tested.Location Test data were taken from Southern Africa.Methods The approach relies mainly on the use of the AUC (Area under the Curve) statistic, based on ROC (threshold Receiver Operating Characteristic) plots, for between-model comparisons. Data for the distribution of the bent tick Amblyomma hebraeum Koch (Acari: Ixodidae) are used to illustrate the methods.Results Methods for the estimation of minimum sample sizes and more accurate hypothesis-testing are outlined. Logistic regression is robust to the assumption that uncollected cells can be scored as negative, provided that the sample size of cells scored as positive is adequate. The variation in temperature and rainfall at localities where A. hebraeum has been collected is significantly lower than expected from a random sample of points across the data set, suggesting that within-site variation may be an important determinant of its distribution.Main conclusions Between-model comparisons relying on AUCs can be used to enhance objectivity in the development and refinement of logistic regression models. Both between-site and within-site variability should be considered as potentially important factors determining species distributions.