Hi All,
I am using the new parametrization for a bivariate model, as suggested in "Type I Error Rates and Parameter Bias in Multivariate Behavioral Genetic Models".
The results (see below) include an rA correlation that is higher than one.
Am I doing something wrong or I have to interpret the rA correlation as 1?
The phenotypic correlation is .2. Is it be correct to say that is due to 1, -.96 and .2 for A, D and E respectively?
There are also negative values: there is no way to interpret these results, right?
Thank you so much in advance
$V mxAlgebra 'V' $formula: VA + VD + VE $result: [,1] [,2] [1,] 0.26015570 0.08651586 [2,] 0.08651586 0.64434885 dimnames: NULL $iSD mxAlgebra 'iSD' $formula: solve(sqrt(I * V)) $result: [,1] [,2] [1,] 1.9605744 0.0000000 [2,] 0.0000000 1.2457746 dimnames: NULL $rA mxAlgebra 'rA' $formula: solve(sqrt(I * VA)) %&% VA $result: [,1] [,2] [1,] 1.0000000 3.1877784 [2,] 3.1877784 1.0000000 dimnames: NULL $rD mxAlgebra 'rD' $formula: solve(sqrt(I * VD)) %&% VD $result: [,1] [,2] [1,] 1.00000000 -0.95732896 [2,] -0.95732896 1.00000000 dimnames: NULL $rE mxAlgebra 'rE' $formula: solve(sqrt(I * VE)) %&% VE $result: [,1] [,2] [1,] 1.00000000 0.20271077 [2,] 0.20271077 1.00000000 dimnames: NULL $US mxAlgebra 'US' $formula: cbind(VA, VD, VE, VA/V, VD/V, VE/V) $result: VA VA VD VD VE VE SA SA SD SD US 0.036015032 0.130102084 0.053615331 -0.096971671 0.170525336 0.053385447 0.13843645 1.503794611 0.2060894 -1.12085427 US 0.130102084 0.046249649 -0.096971671 0.191371989 0.053385447 0.406727210 1.50379461 0.071777344 -1.1208543 0.29700059 SE SE US 0.65547415 0.61705966 US 0.61705966 0.63122206
Hope that you are all well during this difficult situation