Segmented linear regression models applied to the analysis of data from a cross-sectional growth experiment

Several different methods of analysis are applied to data consisting of weight measurements, taken at specified post-treatment times, of harvested thyroids from rats given one of four treatments. Previous studies of this type of data indicated that the growth is initially rapid, and that a second phase of less rapid growth is followed by a final phase in which little additional growth occurs. The data are further characterized by increasing variance through time. The primary purpose of the analysis is to study the effect of the treatments at the end of the study period. One-way analysis of variance tests among groups are performed on each day, but the results are not particularly helpful. However, results from two-way analyses of variance (over subsets of days and groups) are consistent with the three phase model and accordingly indicate significant group differences during each. Finally, maximum likelihood methods are used to fit a three part segmented linear regression model.     

利用各世代小区平均数的加权最小二乘法在玉米数量性状遗传分析上的应用

本文根据作者在参考文献[3]所提出的利用各世代小区平均数估计遗传参数的加权最小二乘法对玉米穗粒重的遗传特性进行了分析。结果表明:(1)在本次试验中,可用加性-显性-二基因互作模型来描述玉米穗粒重的遗传特性;其加性效应[d]、加性效应与显性效应的互作[j]在α=0.01水平上显著,显性效应[h]、显性效应与显性效应的互作[l]在α=0.10水平上显著,加性效应与加性效应的互作[i]不显著;[d]与[h]的差异在α=0.10水平上不显著,但([d] [h])与([i] [j] [l])在α=0.05水平上显著;(2)与利用6个世代平均数相比,利用8个世代小区平均数的加权最小二乘法既节约、又灵敏。