Estimating polymorphic growth curve sets with nonchronological data
Estimating polymorphic growth curve sets with nonchronological data
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用非时间顺序数据估计多态性生长曲线集
DOI：
10.1002/ece3.6528
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发表时间：
2020
影响因子：
2.6
通讯作者：
Moriguchi Kai
中科院分区：
文献类型：
--
作者：
Smith Zach M.;Chase Kevin D.;Takagi Etsuro;Kees Aubree M.;Aukema Brian H.;Moriguchi Kai
当我们收集许多个体的生长曲线时，经常观察到曲线的有序变化，而不是各种曲线的完全随机混合。小个体可能表现出类似的生长曲线，但曲线与大个体的曲线不同，由此曲线从小个体到大个体逐渐变化。人们已经认识到，在用渐近线标准化后，如果所有的生长曲线都相同（变形生长曲线集），则可以使用非时序数据估计生长曲线集;否则，即，如果用渐近线标准化后的生长曲线不相同（多态生长曲线集），则该估计不可行。然而，由于一组给定的生长曲线确定的变化，在观察到的数据，它可能是可能的估计多态生长曲线集使用nonchronological data.In这项研究中，我们开发了一种估计方法，通过推导出的似然函数多态生长曲线集。该方法涉及简单的最大似然估计。将变形生长曲线集对数变换后的加权非线性回归和最小二乘法作为特例，分别估计了柏木和日本落叶松的树高生长曲线集。与模型选择过程中使用AIC和似然比检验，柏树的生长曲线集被发现是多态的，而落叶松被发现是变形的。多形模型对柏树拟合的改进是由于解决了欠分散（真实的数据比模型预测更少的分散）。模型估计的似然函数不仅取决于渐近线的分布类型，而且还取决于生长曲线集的定义。考虑这些因素可能是必要的，即使引入环境解释变量和随机效应。
When we collect the growth curves of many individuals, orderly variation in the curves is often observed rather than a completely random mixture of various curves. Small individuals may exhibit similar growth curves, but the curves differ from those of large individuals, whereby the curves gradually vary from small to large individuals. It has been recognized that after standardization with the asymptotes, if all the growth curves are the same (anamorphic growth curve set), the growth curve sets can be estimated using nonchronological data; otherwise, that is, if the growth curves are not identical after standardization with the asymptotes (polymorphic growth curve set), this estimation is not feasible. However, because a given set of growth curves determines the variation in the observed data, it may be possible to estimate polymorphic growth curve sets using nonchronological data.In this study, we developed an estimation method by deriving the likelihood function for polymorphic growth curve sets. The method involves simple maximum likelihood estimation. The weighted nonlinear regression and least‐squares method after the log‐transform of the anamorphic growth curve sets were included as special cases.The growth curve sets of the height of cypress (Chamaecyparis obtusa) and larch (Larix kaempferi) trees were estimated. With the model selection process using the AIC and likelihood ratio test, the growth curve set for cypress was found to be polymorphic, whereas that for larch was found to be anamorphic. Improved fitting using the polymorphic model for cypress is due to resolving underdispersion (less dispersion in real data than model prediction).The likelihood function for model estimation depends not only on the distribution type of asymptotes, but the definition of the growth curve set as well. Consideration of these factors may be necessary, even if environmental explanatory variables and random effects are introduced.
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