Genetic analysis of discrete reproductive traits in sheep using linear and nonlinear models: II. Goodness of fit and predictive ability.
Genetic analysis of discrete reproductive traits in sheep using linear and nonlinear models: II. Goodness of fit and predictive ability.
复制标题
使用线性和非线性模型对绵羊离散繁殖性状进行遗传分析：II。
DOI：
10.2527/1997.75188x
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发表时间：
1997
影响因子：
3.3
通讯作者：
L. D. Young
中科院分区：
文献类型：
--
作者：
C. Matos;D. L. Thomas;D. Gianola;Miguel Pérez;L. D. Young
线性和非线性的公畜和动物模型的性能进行了比较，在两个绵羊种群（朗布依埃和Finnsheep）的繁殖性状（生育力，产仔数，排卵率）的分析的拟合优度和预测能力。线性公畜（LSM）和动物（LAM）模型与所有性状。非线性模型为阈值、泊松和负二项分布。阈值公畜（TSM）和动物（TAM）模型也用于所有性状。还使用Poisson和负二项公猪（分别为PSM和NBSM）和动物（分别为PAM和NBAM）模型分析了窝仔数和排卵率。方差分量是在配套文章中报告的。对于PAM，还使用了一组新的方差分量（PAM-L），这些方差分量来自线性动物模型的估计值。均方误差（MSE）和拟合值与观察值之间的相关性用于评估拟合优度。通过将不同性状的数据集划分为两个子集来评估预测能力，限制是每个子集中代表了所有水平的固定效应。一个子集的参数被用来预测另一个子集的观测值，然后将观测值和预测值之间的MSE和相关性用作模型比较的标准。在估计程序、品种和性状方面，公畜模型和动物模型的拟合优度相似。线性和阈值模型导致相似的拟合，并且都优于泊松和负二项模型。在预测能力方面，线性和阈值模型仅略优于泊松和负二项模型。当模型包括永久性环境影响时，拟合优度和预测能力通常更好。
The performance of linear and nonlinear sire and animal models in the analyses of reproductive traits (fertility, litter size, and ovulation rate) in two sheep populations (Rambouillet and Finnsheep) was compared in terms of goodness of fit and predictive ability. Linear sire (LSM) and animal (LAM) models were used with all traits. Nonlinear models were the threshold, Poisson, and negative binomial. Threshold sire (TSM) and animal (TAM) models were also used with all traits. Litter size and ovulation rate were analyzed also with Poisson and negative binomial sire (PSM and NBSM, respectively) and animal (PAM and NBAM, respectively) models. Variance components were those reported in the companion article. For PAM a new set of variance components derived from estimates found with the linear animal model also was used (PAM-L). Mean squares error (MSE) and correlations between fitted and observed values were used to assess goodness of fit. Predictive ability was assessed by partitioning the data sets for the different traits into two subsets with the restriction that all levels of fixed effects were represented in each subset. Parameters from one subset were employed to predict observations in the other, and then MSE and correlations between observed and predicted values were used as criteria for model comparison. Within estimation procedure, breed, and trait, goodness of fit of sire and animal models was similar. Linear and threshold models resulted in similar fit, and both outperformed Poisson and negative binomial models. In terms of predictive ability, linear and threshold models performed only slightly better than Poisson and negative binomial models. Goodness of fit and predictive ability generally were better when models included permanent environmental effects.