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The factorial co-heritabilities can be estimated as shown below (shortened output). Note that for the first genetic factor in a Cholesky model, the factorial co-heritability is always **one**, as per definition, this factor explains the entire genetic variation of the trait. Note that `Vi` is the estimated explained trait variance by a factor and corresponds to the squared path coefficient.
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```{r eval=FALSE}
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> gsem.fcoher(fit.large)
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> gsem.fcoher(fit.large)$fcoher.model.out[,c(1,7:10)]
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> grmsem.fcoher(fit.large)
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> grmsem.fcoher(fit.large)$fcoher.model.out[,c(1,7:10)]
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label Vi Vi.se FCOHER FCOHER.se
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1 a11 3.484351e-01 0.0223563431 1.0000000000 0.000000000
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... | ... | @@ -31,8 +31,8 @@ The factorial co-heritabilities can be estimated as shown below (shortened outpu |
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Similarly, the factorial co-environmentalities is shown below (shortened output). Again, note that for the first residual factor in a Cholesky model, the factorial co-environmentality is always **one**, as per definition this factor explains the entire residual variation of the trait.
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```{r eval=FALSE}
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> gsem.fcoher(fit.large)
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> gsem.fcoher(fit.large)$fcoher.model.out[,c(1,7:8,13:14)]
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> grmsem.fcoher(fit.large)
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> grmsem.fcoher(fit.large)$fcoher.model.out[,c(1,7:8,13:14)]
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label Vi Vi.se FCOENV FCOENV.se
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1 a11 3.484351e-01 0.0223563431 NA NA
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... | ... | |