LOG-NORMAL VARIATION BELTS FOR GROWTH-CURVES
LOG-NORMAL VARIATION BELTS FOR GROWTH-CURVES
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DOI：
10.2307/2530693
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发表时间：
1986-12-01
期刊：
影响因子：
1.9
通讯作者：
HEUSNER, AA
中科院分区：
文献类型：
--
作者：
JOLICOEUR, P;HEUSNER, AA
预测(置信度)或容差带使样本估计的不确定性与个体差异的估计程度相结合。因此，后者更好地用变化带来描述，在变化带中，样本估计值简单地用来代替总体参数。变化带可以提供关于假设误差模型的拟合优度的有价值的图形指示。虽然乘性最小二乘(MLS)方法在原则上似乎适合于生物生长，但在对数变换的数据是异方差的情况下，它们在实践中并不令人满意。异方差乘性误差模型可以迭代地拟合加权乘性最小二乘(IRMLS)，但有时会得到不可接受的负或无限残差估计和不合理的宽变化带。这些困难可以通过约束迭代加权乘法最小二乘(CIRMLS)来避免。给出了大白鼠代谢异速生长、雄性象海豹体细胞生长和尾草履虫实验种群生长的例子。
Prediction (confidence) or tolerance belts compound the uncertainty of sample estimates with the estimated extent of individual variation. The latter is therefore better described by variation belts, in which sample estimates are simply substituted for population parameters. Variation belts can provide valuable graphical indications concerning the goodness of fit of postulated error models. While multiplicative least-squares (MLS) methods appear appropriate in principle for biological growth, they are unsatisfactory in practice when logarithmically transformed data are heteroscedastic. Heteroscedastic multiplicative error models can be fitted iteratively reweighted multiplicative least squares (IRMLS), but unacceptable negative or infinite residual variance estimates and unreasonably wide variation belts are occasionally obtained. These difficulties can be prevented by constrained iteratively reweighted multiplicative least squares (CIRMLS). Examples are presented concerning the metabolic allometry of white rats, the somatic growth of male elephant seals, and the growth of an experimental population of Paramecium caudatum.