MVP.EMMA.Vg.Ve {rMVP} | R Documentation |
Build date: August 30, 2016 Last update: January 27, 2017
MVP.EMMA.Vg.Ve(y, X, K, ngrids = 100, llim = -10, ulim = 10, esp = 1e-10)
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 |
Output: REML - maximum log likelihood Output: delta - exp(root) Output: ve - residual variance Output: vg - genetic variance
EMMA (Kang et. al. Genetics, 2008), Modified only for speed up by Xiaolei Liu and Lilin Yin
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) vc <- MVP.EMMA.Vg.Ve(y=phenotype[,2], X=matrix(1, nrow(phenotype)), K=K) print(vc)