Measurement Error Revisited: Its Importance for the Analysis of Size and Shape of Birds
Measurement Error Revisited: Its Importance for the Analysis of Size and Shape of Birds
复制标题
重新审视测量误差：其对于分析鸟类大小和形状的重要性
DOI：
10.3161/000164510x551309
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
A. Gosler
中科院分区：
文献类型：
--
作者：
Utku Perktaş;A. Gosler
抽象的。
形态性状的测量误差是许多鸟类学研究（如生态形态学、遗传力的定量研究、系统变异和地理变异研究）中的一个重要问题。外部形态特征的变异，如翅长和跗长，通常使用多变量统计方法如主成分分析（PCA）进行评估。这些方法通常被认为比单变量统计方法更好地解释鸟类种群的大小和形状变化，因为它们减少了数据的“维度”-个体测量的大小（翅膀等）。被假定为包含反映通用字符“size”的组件。然而，测量误差对主成分的影响尚未正式评估这些数据。在这里，我们报告了三个例子，以评估测量误差的重要性，在鸟类种群内和之间的分析。测量误差对PCA的影响也进行了讨论，在形状组件中的误差水平的重要性。我们的研究结果表明，相对于大小（PC 1），主成分分数的影响较小的测量误差，如果使用的协方差矩阵，而不是相关矩阵。然而，相对测量误差的影响在随后的轴中显著大于它们在尺寸轴（PC 1）中的影响，随后的轴代表形状变化而不是尺寸。因此，测量误差对于形状轴可能是比对于尺寸轴更重要的问题，并且如果在PCA中使用非常少的字符，则该问题可能进一步加剧。我们的研究结果还表明，PCA是特别敏感的样本量有关的问题。我们建议，如果无法减少尺寸和形状测量的测量误差，并且样本量较小（≤ 30），则应使用协方差矩阵推导主成分评分，因为这些更有可能给出更稳健的结果。
Abstract.
Measurement error in morphological characters is an important issue for many ornithological studies (e.g. ecomorphology, quantitative studies of heritability, studies of systematic and geographic variation). The variation in external morphological characters, such as wing and tarsus length, is usually evaluated using multivariate statistical methods such as principal component analysis (PCA). These are often considered better than univariate statistical methods for explaining size and shape variation in bird populations because they reduce the ‘dimensionality’ of the data — the size of individual measures (wing etc.) are assumed to contain a component reflecting a general character ‘size’. However, the effect of measurement error on principal components has not been formally assessed with respect to such data. Here we report three examples in order to assess the importance of measurement error for analyses within and between bird populations. The effect of measurement error on PCA is also discussed in relation to the importance of levels of error in shape components. Our results indicate that, in relation to size (PC1), principal component scores are affected less by measurement error if the covariance matrix is used rather than the correlation matrix. However, the effects of relative measurement error were substantially greater in subsequent axes, which represent shape variation rather than size, than they were in the size axis (PC1). Measurement error may, therefore, be a more important issue for shape axes than for the size axis and this problem may be exacerbated further if very few characters are used in the PCA. Our results also indicate that PCA is especially sensitive to issues relating to sample size. We recommend that if reducing the measurement error in size and shape measures is not possible, and the sample size is small (≤ 30), principal component scores should be derived using the covariance matrix, as these are more likely to give more robust results.
DOI：
10.1093/auk/106.4.666
发表时间：
1989-10
期刊：
The Auk
影响因子：
--
作者：
J. Rising;K. Somers
通讯作者：
J. Rising;K. Somers