MVP.MLM {rMVP} | R Documentation |
Build date: Aug 30, 2016 Last update: Aug 30, 2016
MVP.MLM(phe, geno, K = NULL, eigenK = NULL, CV = NULL, REML = NULL, cpu = 1, bar = TRUE, vc.method = c("BRENT", "EMMA", "HE"), verbose = TRUE)
phe |
phenotype, n * 2 matrix |
geno |
genotype, m * n, m is marker size, n is population size |
K |
Kinship, Covariance matrix(n * n) for random effects; must be positive semi-definite |
eigenK |
list of eigen Kinship |
CV |
covariates |
REML |
a list that contains ve and vg |
cpu |
number of cpus used for parallel computation |
bar |
whether to show the progress bar |
vc.method |
the methods for estimating variance component("emma" or "he" or "brent") |
verbose |
whether to print detail. |
results: a m * 2 matrix, the first column is the SNP effect, the second column is the P values
Lilin Yin and Xiaolei Liu
phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP") phenotype <- read.table(phePath, header=TRUE) idx <- !is.na(phenotype[, 2]) phenotype <- phenotype[idx, ] print(dim(phenotype)) genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP") genotype <- attach.big.matrix(genoPath) genotype <- genotype[, idx] print(dim(genotype)) K <- MVP.K.VanRaden(genotype) mlm <- MVP.MLM(phe=phenotype, geno=genotype, K=K) str(mlm)