MVP.FarmCPU {rMVP} | R Documentation |
Date build: Febuary 24, 2013 Last update: May 25, 2017 Requirement: Y, GD, and CV should have same taxa order. GD and GM should have the same order on SNPs
MVP.FarmCPU(phe, geno, map, CV = NULL, P = NULL, method.sub = "reward", method.sub.final = "reward", method.bin = "static", bin.size = c(5e+05, 5e+06, 5e+07), bin.selection = seq(10, 100, 10), memo = "MVP.FarmCPU", Prior = NULL, ncpus = 2, bar = TRUE, maxLoop = 10, threshold.output = 0.01, converge = 1, iteration.output = FALSE, p.threshold = NA, QTN.threshold = 0.01, bound = NULL, verbose = TRUE)
phe |
phenotype, n by t matrix, n is sample size, t is number of phenotypes |
geno |
genotype, m by n matrix, m is marker size, n is sample size. This is Pure Genotype Data Matrix(GD). THERE IS NO COLUMN FOR TAXA. |
map |
SNP map information, m by 3 matrix, m is marker size, the three columns are SNP_ID, Chr, and Pos |
CV |
covariates, n by c matrix, n is sample size, c is number of covariates |
P |
start p values for all SNPs |
method.sub |
method used in substitution process, five options: 'penalty', 'reward', 'mean', 'median', or 'onsite' |
method.sub.final |
method used in substitution process, five options: 'penalty', 'reward', 'mean', 'median', or 'onsite' |
method.bin |
method for selecting the most appropriate bins, two options: 'EMMA' or 'FaSTLMM' |
bin.size |
bin sizes for all iterations, a vector, the bin size is always from large to small |
bin.selection |
number of selected bins in each iteration, a vector |
memo |
a marker on output file name |
Prior |
prior information, four columns, which are SNP_ID, Chr, Pos, P-value |
ncpus |
number of threads used for parallele computation |
bar |
if TRUE, the progress bar will be drawn on the terminal |
maxLoop |
maximum number of iterations |
threshold.output |
only the GWAS results with p-values lower than threshold.output will be output |
converge |
a number, 0 to 1, if selected pseudo QTNs in the last and the second last iterations have a certain probality (the probability is converge) of overlap, the loop will stop |
iteration.output |
whether to output results of all iterations |
p.threshold |
if all p values generated in the first iteration are bigger than p.threshold, FarmCPU stops |
QTN.threshold |
in second and later iterations, only SNPs with lower p-values than QTN.threshold have chances to be selected as pseudo QTNs |
bound |
maximum number of SNPs selected as pseudo QTNs in each iteration |
verbose |
whether to print detail. |
a m by 4 results matrix, m is marker size, the four columns are SNP_ID, Chr, Pos, and p-value
Xiaolei Liu and Zhiwu Zhang
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)) mapPath <- system.file("extdata", "07_other", "mvp.map", package = "rMVP") map <- read.table(mapPath , head = TRUE) farmcpu <- MVP.FarmCPU(phe=phenotype, geno=genotype, map=map, maxLoop=2) str(farmcpu)