Pasternak H, Shalev B A
Agricultural Engineering Institute, Agricultural Research Organization, Bet Dagan, Israel.
Growth Dev Aging. 1994 Spring;58(1):33-9.
Growth curve parameters are usually estimated by employing non-linear regression. In the present study this method was found to be inefficient for fitting growth curves, since the magnitude of random deviations of body weight greatly increases with age (heteroskedastic regression disturbance). Simulated samples of broiler body weights at different ages were generated and the associated Gompertz growth curve parameters were estimated employing three methods. Comparison of the efficiency of these methods in fitting Gompertz growth curve under this regression disturbance were performed. The results indicate that the most efficient method to estimate growth curve parameters is "weighted non-linear regression". The efficiency of this method was found to be much higher than that of conventional non-linear regression. These findings should be taken into consideration when fitting growth curves, in general, as well as for the Gompertz equation.
生长曲线参数通常采用非线性回归进行估计。在本研究中，发现该方法在拟合生长曲线时效率低下，因为体重的随机偏差幅度会随着年龄的增长而大幅增加（异方差回归扰动）。生成了不同年龄肉鸡体重的模拟样本，并采用三种方法估计了相关的冈珀茨生长曲线参数。对这些方法在这种回归扰动下拟合冈珀茨生长曲线的效率进行了比较。结果表明，估计生长曲线参数最有效的方法是“加权非线性回归”。发现该方法的效率远高于传统的非线性回归。在一般情况下拟合生长曲线以及拟合冈珀茨方程时，都应考虑这些发现。
