General purpose model and a computer program for combined segregation and path analysis (SEGPATH): automatically creating computer programs from symbolic language model specifications.
General purpose model and a computer program for combined segregation and path analysis (SEGPATH): automatically creating computer programs from symbolic language model specifications.
复制标题
用于组合隔离和路径分析的通用模型和计算机程序（SEGPATH）：根据符号语言模型规范自动创建计算机程序。
DOI：
10.1002/gepi.1370120208
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
Rao,DC
中科院分区：
文献类型：
--
作者：
Province,MA;Rao,DC
一个通用的模型和一个灵活的计算机程序，称为SEGPATH，已被开发出来，以协助创建和实施各种遗传流行病学模型。SEGPATH是一种计算机程序，可用于生成程序，以基于灵活的模型规范语法实现谱系数据的线性模型。SEGPATH模型可以使用任何用户指定的路径模型进行分离分析、路径分析或组合分离和路径分析，并且可以被构造为分析任何数量的多变量表型、环境指数和/或测量的协变量固定效应（包括测量的基因型）。群体异质性模型、重复测量模型、纵向模型、自回归模型、发育模型和基因与环境相互作用模型都可以在SEGPATH下创建。系谱结构可以被定义为任意复杂的，并且用SEGPATH生成的程序分析的数据可以具有任何缺失值结构，整个个体缺失，或者在一个或多个测量中缺失。可以对表型和/或其他测量的载体进行确定的校正。由于模型规范语法是通用的，因此SEGPATH也可用于具有层次结构的非遗传应用，例如纵向、重复测量、时间序列或嵌套模型。展示了各种应用。© 1995 Wiley利斯公司
A general purpose model and a flexible computer program, called SEGPATH, have been developed to assist in the creation and implementation of a variety of genetic epidemiological models. SEGPATH is a computer program which can be used to generate programs to implement linear models for pedigree data, based upon a flexible, model‐specification syntax. SEGPATH models can perform segregation analysis, path analysis, or combined segregation and path analysis using any user‐specified path model and can be structured to analyze any number of multivariate phenotypes, environmental indices, and/or measured covariate fixed effects (including measured genotypes). Population heterogeneity models, repeated‐measures models, longitudinal models, auto‐regressive models, developmental models, and gene‐by‐environment interaction models can all be created under SEGPATH. Pedigree structures can be defined to be arbitrarily complex, and the data analyzed with programs generated by SEGPATH can have any missing value structure, with entire individuals missing, or missing on one or more measurements. Corrections for ascertainment can be done on a vector of phenotypes and/or other measures. Because the model specification syntax is general, SEGPATH can also be used in non‐genetic applications where there is a hierarchical structure, such as longitudinal, repeated‐measures, time series, or nested models. A variety of applications are demonstrated. © 1995 Wiley‐Liss, Inc.