res_se: Calculate the standard error for the residual correlation...

Description Usage Arguments Details Value

Description

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.

Usage

1
res_se(Ve1, VarVe1, Ve2, VarVe2, Ce, VarCe, CovVe1Ve2, CovVe1Ce, CovVe2Ce)

Arguments

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

Details

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.

Value

Standard error of residual correlation between trait 1 and 2


aneumann-science/molepi documentation built on May 10, 2019, 11:46 a.m.