WenS {QTL.gCIMapping.GUI}

The second step of Wen method

Description

An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2

Usage

WenS(flag,CriLOD,NUM,pheRaw,Likelihood,setseed,flagrqtl,yygg,mx,phe,
chr_name,v.map,gen.raw,a.gen.orig,d.gen.orig,n,names.insert2,X.ad.tran.data,X.ad.t4,dir)

Arguments

flag

random or fix model.

CriLOD

LOD score.

NUM

the number of trait.

pheRaw

raw phenotype matrix .

Likelihood

likelihood function.

setseed

random seed set in which, the cross validation is needed.

flagrqtl

do CIM or not.

yygg

covariate matrix.

mx

raw genotype matrix.

phe

phenotype matrix.

chr_name

chromosome name.

v.map

linkage map matrix.

gen.raw

raw genotype matrix.

a.gen.orig

additive genotype matrix.

d.gen.orig

dominant genotype matrix.

n

number of individual.

names.insert2

linkage map after insert.

X.ad.tran.data

genotype matrix after insert.

X.ad.t4

genotype matrix.

dir

file storage path.

Author(s)

Zhang Ya-Wen, Wen Yang-Jun, Wang Shi-Bo, Zhang Yuan-Ming
Maintainer: Yuanming Zhang<[e-mail]>

Examples

## Not run: 
data(genf2)
data(phef2)
data(mapf2)
WEN1re<-WenF(pheRaw=phef2,genRaw=genf2,mapRaw1=mapf2,
yygg1=NULL,cov_en=NULL,WalkSpeed=1,CriLOD=2.5,dir=tempdir())
###
ws<-WenS(flag=1,CriLOD=2.5,NUM=1,pheRaw=phef2,
Likelihood="REML",setseed=11001,flagrqtl=FALSE,
yygg=WEN1re$yygg,mx=WEN1re$mx,phe=WEN1re$phe,
chr_name=WEN1re$chr_name,v.map=WEN1re$v.map,
gen.raw=WEN1re$gen.raw,a.gen.orig=WEN1re$a.gen.orig,
d.gen.orig=WEN1re$d.gen.orig,n=WEN1re$n,
names.insert2=WEN1re$names.insert2,
X.ad.tran.data=WEN1re$X.ad.tran.data,
X.ad.t4=WEN1re$X.ad.t4,dir=tempdir())

## End(Not run)