varcomp: Estimates of variance componets.
In HaipengU/GCA_r: Genetic Connectedness Analysis

Description Usage Arguments Value Author(s) Examples

Maximum Likelihood estimation of variance components using the eigenvalues and eigenvectors, which are derived from the eigendecomposition of the relationship matrix.

1	varcomp(y, Evector, Evalue)

`y`	A vector includes the phenotypes of n individuals.
`Evector`	A matrix (n x n) with columns according to eigenvectors from the eigendecomposition of the relationship matrix.
`Evalue`	A vector which contains the eigenvalues of n individuals from the eigendecomposition of the relationship matrix.

A list contains variance components

$Ve: An estimate of residual variance.
$Vu: An estimate of additive genetic variance.

Haipeng Yu and Gota Morota

Maintainer: Haipeng Yu haipengyu@vt.edu

# Load cattle data
data(GCcattle)

# Phenotype, fixed covariates and marker information
str(cattle.pheno)
str(cattle.W)

# Compute genomic relationship matrix
G <- computeG(cattle.W, maf = 0.05, impute = 'mean', method = 'G1')

# Eigendecomposition of genomic relationship matrix
EVD <- eigen(G)

# Estimate variance component
var <- varcomp(y = cattle.pheno$Phenotype, Evector = EVD$vectors, Evalue = EVD$values) 

# Retrieve additive genetic variance and residual variance
var$Vu
var$Ve