Description Usage Arguments Details Value
write_gpf_syntax
Bivariate GREML as implemented in GCTA outputs
genetic correlation but not residual correlation. This function calculates
the standard error of the residual correlation based on the variances and
covariances of both traits. See covariance matrix of GCTA output.
1 | res_se(Ve1, VarVe1, Ve2, VarVe2, Ce, VarCe, CovVe1Ve2, CovVe1Ce, CovVe2Ce)
|
Ve1 |
A numeric value. The residual variance of trait 1. |
VarVe1 |
A numeric value. Sampling variance of trait 1. Can be found in 1,1 of covariance matrix. |
Ve2 |
A numeric value. The residual variance of trait 2. |
VarVe2 |
A numeric value. The sampling variance of trait 2. Can be found in 2,2 of covariance matrix. |
Ce |
A numeric value. The residual covariance between trait 1 and 2. |
VarCe |
A numeric value. Sampling Variance of Ce [square(SE of C(e)_tr2)]. Can be found in 3,3 of covariance matrix. |
CovVe1Ve2 |
A numeric value. Sampling covariance between Ve1 and Ve2. Can be found in 5,4 in Covariance Matrix |
CovVe1Ce |
A numeric value. Sampling covariance between Ve1 and Ce. Can be found in 6,4 in Covariance Matrix. |
CovVe2Ce |
A numeric value. Sampling covariance between Ve2 and Ce. Can be found in 6,5 in Covariance Matrix |
Formula from Trzaskowski, M., Yang, J., Visscher, P. M., & Plomin, R. (2013). DNA evidence for strong genetic stability and increasing heritability of intelligence from age 7 to 12. Molecular psychiatry, 19(3), 380-384.
Standard error of residual correlation between trait 1 and 2
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.