Effect of uneven sampling along an environmental gradient on transfer-function performance
Effect of uneven sampling along an environmental gradient on transfer-function performance
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DOI：
10.1007/s10933-011-9523-z
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发表时间：
2011-04
影响因子：
2.1
通讯作者：
Richard J. Telford;Richard J. Telford;H. J. B. Birks;H. J. B. Birks;H. J. B. Birks
中科院分区：
文献类型：
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作者：
Richard J. Telford;Richard J. Telford;H. J. B. Birks;H. J. B. Birks;H. J. B. Birks
我们调查的效果，不均匀采样的环境梯度上的传递函数的性能，使用模拟社区数据。我们发现，交叉验证的估计预测的均方根误差可以很强的偏见，如果观察是非常不均匀的分布沿着环境梯度。这种偏差的发生是因为物种最优值在具有大多数观测的梯度部分中更精确地已知（并且更多的类似物可用），因此这里的估计是最精确的，并且补偿了梯度的采样较少的部分中的较不精确的估计。我们发现，加权平均和现代模拟技术比最大似然法对这个问题更敏感，并建议通过分段RMSEP程序来消除偏差。
We investigate the effect that uneven sampling of the environmental gradient has on transfer-function performance using simulated community data. We find that cross-validated estimates of the root mean squared error of prediction can be strongly biased if the observations are very unevenly distributed along the environmental gradient. This biased occurs because species optima are more precisely known (and more analogues are available) in the part of the gradient with most observations, hence estimates are most precise here, and compensate for the less precise estimates in the less well sampled parts of the gradient. We find that weighted averaging and the modern analogue technique are more sensitive to this problem than maximum likelihood, and suggest a way to remove the bias via a segment-wise RMSEP procedure.