MVP.BRENT.Vg.Ve: MVP.BRENT.Vg.Ve variance component estimation using the BRENT...

In rMVP: Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool

MVP.BRENT.Vg.Ve variance component estimation using the BRENT method

Description

MVP.BRENT.Vg.Ve variance component estimation using the BRENT method

Usage

MVP.BRENT.Vg.Ve(y, X, eigenK, verbose = FALSE)

Arguments

y	phenotype
X	covariate matrix, the first column is 1s
eigenK	eigen of Kinship matrix
verbose	whether to print detail.

Value

vg, ve, and delta

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))

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

rMVP documentation built on Nov. 27, 2023, 5:09 p.m.