THE IMPORTANCE OF CONSIDERING PREDICTION VARIANCE IN ANALYSES USING PHOTOGRAMMETRIC MASS ESTIMATES
THE IMPORTANCE OF CONSIDERING PREDICTION VARIANCE IN ANALYSES USING PHOTOGRAMMETRIC MASS ESTIMATES
复制标题
在使用摄影测量质量估计进行分析时考虑预测方差的重要性
DOI：
10.1111/j.1748-7692.2006.00091.x
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发表时间：
2007
影响因子：
2.3
通讯作者：
J. Banfield
中科院分区：
文献类型：
--
作者：
K. Proffitt;R. Garrott;J. Rotella;J. Banfield
在海洋哺乳动物研究中，摄影测量质量估计技术的开发和应用越来越普遍。当使用摄影测量估计的质量作为回归建模中的协变量时，与估计质量相关的误差会导致回归统计中的偏差，并降低模型的解释力。因此，重要的是要了解和解释预测方差时，解决生态问题，需要使用估计的质量值。在一项模拟研究的基础上收集的数据从威德尔海豹，我们开发的回归模型的幼崽断奶质量作为母亲产后质量的函数，母亲的质量是直接测量和第二母亲的质量摄影估计。我们证明，当估计的质量被使用时，回归系数偏向于零，决定系数是30%，小于使用产妇产后质量直接测量时获得的值。然而，在应用偏倚校正程序后，回归系数和决定系数在其真实值的2%以内。为了有效地使用摄影测量估计的质量，在所有分析中应理解和考虑预测方差。本文提出的方法是探索和解释预测方差的有效而简单的技术。
Development and application of photogrammetric mass-estimation techniques in marine mammal studies is becoming increasingly common. When a photogrammetrically estimated mass is used as a covariate in regression modeling, the error associated with estimating mass induces bias in regression statistics and decreases model explanatory power. Thus, it is important to understand and account for prediction variance when addressing ecological questions that require use of estimated mass values. In a simulation study based on data collected from Weddell seals, we developed regression models of pup weaning mass as a function of maternal postparturition mass where maternal mass was directly measured and second where maternal mass was photogrammetrically estimated. We demonstrate that when estimated mass was used, the regression coefficient was biased toward zero and the coefficient of determination was 30% less than the value obtained when using maternal postparturition mass obtained from direct measurement. After applying bias correction procedures, however, the regression coefficient and coefficient of determination were within 2% of their true values. To effectively use photogrammetrically estimated masses, prediction variance should be understood and accounted for in all analyses. The methods presented in this paper are effective and simple techniques to explore and account for prediction variance.