Popular Structural Equation Modeling Programs for Behavior Genetics     

S. Mason Garrison 《Structural equation modeling》2018,25(6):972-977

Behavior genetic modeling is a prominent application of multi-group structural equation modeling (SEM). It decomposes phenotypic variance into genetic and environmental sources by leveraging the covariation within and between kin pairs. Although any SEM program with multi-group capabilities can be employed, the software program, Mx, has dominated behavior genetics research. Indeed, even though Mx has not been maintained since 2011, it remains the most popular SEM program in Behavior Genetics articles published in 2016 and 2017. Given the persistence of Mx, the aim of this article is to understand Mx’s performance relative to other popular behavior genetic programs. Through this process, programs employed in behavior genetics research are identified, and their relevant technical features and accessibility are compared. Finally, the relative strengths and limitations of the programs are discussed, and recommendations are provided for behavior genetics researchers.  相似文献   

Fusion Via a Linear Combination of Scores   总被引：9，自引：2，他引：7  

Christopher C. Vogt  Garrison W. Cottrell 《Information Retrieval》1999,1(3):151-173

We present a thorough analysis of the capabilities of the linear combination (LC) model for fusion of information retrieval systems. The LC model combines the results lists of multiple IR systems by scoring each document using a weighted sum of the scores from each of the component systems. We first present both empirical and analytical justification for the hypotheses that such a model should only be used when the systems involved have high performance, a large overlap of relevant documents, and a small overlap of nonrelevant documents. The empirical approach allows us to very accurately predict the performance of a combined system. We also derive a formula for a theoretically optimal weighting scheme for combining 2 systems. We introduce d—the difference between the average score on relevant documents and the average score on nonrelevant documents—as a performance measure which not only allows mathematical reasoning about system performance, but also allows the selection of weights which generalize well to new documents. We describe a number of experiments involving large numbers of different IR systems which support these findings.  相似文献