MVP.HE.Vg.Ve: To estimate variance component using HE regression

In XiaoleiLiuBio/MVP: Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool

To estimate variance component using HE regression

Description

Build date: Feb 2, 2017 Last update: Feb 2, 2019

Usage

MVP.HE.Vg.Ve(y, X, K)

Arguments

y	phenotype
X	genotype
K	kinship matrix

Value

vg, ve, and delta

Author(s)

Translated from C++(GEMMA, Xiang Zhou) to R by: Haohao Zhang

Examples

phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP")
phenotype <- read.table(phePath, header=TRUE)
print(dim(phenotype))
genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
genotype <- attach.big.matrix(genoPath)
print(dim(genotype))

K <- MVP.K.VanRaden(genotype, cpu=1)
vc <- MVP.HE.Vg.Ve(y=phenotype[,2], X=matrix(1, nrow(phenotype)), K=K)
print(vc)

XiaoleiLiuBio/MVP documentation built on Sept. 27, 2024, 7:44 a.m.