MVP.EMMA.Vg.Ve: Estimate variance components using EMMA

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

Estimate variance components using EMMA

Description

Build date: August 30, 2016 Last update: January 27, 2017

Usage

MVP.EMMA.Vg.Ve(y, X, K, ngrids = 100, llim = -10, ulim = 10, esp = 1e-10)

Arguments

y	phenotype, n * 2
X	covariate matrix, the first column is 1s
K	Kinship matrix
ngrids	parameters for estimating vg and ve
llim	parameters for estimating vg and ve
ulim	parameters for estimating vg and ve
esp	parameters for estimating vg and ve

Value

Output: REML - maximum log likelihood Output: delta - exp(root) Output: ve - residual variance Output: vg - genetic variance

Author(s)

EMMA (Kang et. al. Genetics, 2008), Modified only for speed up by Xiaolei Liu and Lilin Yin

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.EMMA.Vg.Ve(y=phenotype[,2], X=matrix(1, nrow(phenotype)), K=K)
print(vc)

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