polcoeff <- c(1.000, .125, .325, .425) # values from .res file
rrmat <- matrix(c(
7.695 , 0.2049 , -0.7956 , 0.7513,
0.9376 , 2.721 , 0.3095 , -0.3744,
-3.456 , 0.7996 , 2.453 , -0.7301,
3.042 , -0.9014 , -1.669 , 2.131 ),4,4)
# values from .asr file (but Upper is correlation)
rrmat[lower.tri(rrmat)]<- 0
rrmat <- rrmat+t(rrmat)-diag(diag(rrmat))
rrmat
[1,] 7.6950 0.9376 -3.4560 3.0420
[2,] 0.9376 2.7210 0.7996 -0.9014
[3,] -3.4560 0.7996 2.4530 -1.6690
[4,] 3.0420 -0.9014 -1.6690 2.1310
>
t(polcoeff) %*% rrmat %*% polcoeff
[,1]
[1,] 8.463358
Thus you have the genetic variance for the age corresponding to the coefficients used.
Then proceed with the heritability depending on the structure of the rest of the model.
ARG 25 Oct 2008
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