NDLI: The Intraclass Covariance Matrix

Content Provider	Springer Nature Link
Author	Carey, Gregory
Copyright Year	2005
Abstract	Introduced by C.R. Rao in 1945, the intraclass covariance matrix has seen little use in behavioral genetic research, despite the fact that it was developed to deal with family data. Here, I reintroduce this matrix, and outline its estimation and basic properties for data sets on pairs of relatives. The intraclass covariance matrix is appropriate whenever the research design or mathematical model treats the ordering of the members of a pair as random. Because the matrix has only one estimate of a population variance and covariance, both the observed matrix and the residual matrix from a fitted model are easy to inspect visually; there is no need to mentally average homologous statistics. Fitting a model to the intraclass matrix also gives the same log likelihood, likelihood-ratio (LR) χ2, and parameter estimates as fitting that model to the raw data. A major advantage of the intraclass matrix is that only two factors influence the LR χ2—the sampling error in estimating population parameters and the discrepancy between the model and the observed statistics. The more frequently used interclass covariance matrix adds a third factor to the χ2—sampling error of homologous statistics. Because of this, the degrees of freedom for fitting models to an intraclass matrix differ from fitting that model to an interclass matrix. Future research is needed to establish differences in power—if any—between the interclass and the intraclass matrix.
Starting Page	667
Ending Page	670
Page Count	4
File Format	PDF
ISSN	00018244
Journal	Behavior Genetics
Volume Number	35
Issue Number	5
e-ISSN	15733297
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2005-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Twins Sib-Pairs linear models covariance matrix correlation matrix interclass covariance intraclass covariance Human Genetics Neurosciences Evolutionary Biology
Content Type	Text
Resource Type	Article
Subject	Genetics Ecology, Evolution, Behavior and Systematics Genetics (clinical)

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