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Comparing observed phenotypic variance/covariance patterns with estimated genetic variance/covariance patterns, we can estimate bivariate heritabilities.
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Note that the diagonal elements of the estimated `BIHER` matrix detail the heritabilities, while the off-diagonal elements detail the co-heritabilities.
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```{r eval = FALSE}
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> fit.var <- gsem.var(fit)
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> load("ph.large")
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> gsem.biher(ph, fit.var)
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$VPO
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1 2 3 4
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Y1 1.0000000 0.5061277 0.6278417 0.1792348
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Y2 0.5061277 1.0000000 0.6167281 0.4352479
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Y3 0.6278417 0.6167281 1.0000000 0.3331679
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Y4 0.1792348 0.4352479 0.3331679 1.0000000
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$VA
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1 2 3 4
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Y1 0.3484351 0.2397902 0.2433639 0.1482684
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Y2 0.2397902 0.5394087 0.4198747 0.3507607
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Y3 0.2433639 0.4198747 0.5366833 0.2642885
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Y4 0.1482684 0.3507607 0.2642885 0.6588525
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$BIHER
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1 2 3 4
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Y1 0.3484351 0.4737741 0.3876197 0.8272299
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Y2 0.4737741 0.5394087 0.6808100 0.8058872
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Y3 0.3876197 0.6808100 0.5366833 0.7932591
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Y4 0.8272299 0.8058872 0.7932591 0.6588525
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$BIHER.se
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1 2 3 4
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Y1 0.02235634 0.03281612 0.02814933 0.08451862
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Y2 0.03281612 0.02017283 0.02711842 0.03588862
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Y3 0.02814933 0.02711842 0.02042099 0.04548383
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Y4 0.08451862 0.03588862 0.04548383 0.02078249
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$BIHER.Z
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1 2 3 4
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Y1 15.585514 14.43723 13.77012 9.787546
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Y2 14.437234 26.73937 25.10508 22.455231
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Y3 13.770125 25.10508 26.28097 17.440466
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Y4 9.787546 22.45523 17.44047 31.702288
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$BIHER.p
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1 2 3 4
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Y1 9.132538e-55 3.017077e-47 3.855470e-43 1.273488e-22
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Y2 3.017077e-47 1.641347e-157 4.377379e-139 1.137653e-111
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Y3 3.855470e-43 4.377379e-139 3.165351e-152 4.067453e-68
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Y4 1.273488e-22 1.137653e-111 4.067453e-68 1.444882e-220
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``` |
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\ No newline at end of file |