qdg.perm.test: Conduct permutation test for LOD score of edge direction on...

In qtlnet: Causal Inference of QTL Networks

Description

Conduct permutation test for LOD score of edge direction on directed graph.

Usage

qdg.perm.test(cross, nperm, node1, node2, common.cov = NULL,
  DG, QTLs, addcov = NULL, intcov = NULL)
## S3 method for class 'qdg.perm.test'
summary(object, ...)
## S3 method for class 'qdg.perm.test'
print(x, ...)

Arguments

cross	Object of class cross (see read.cross).
nperm	Number of permutations.
node1	Character string with name of a phenotype nodes.
node2	Character string with name of a phenotype nodes.
common.cov	Character string with name of common phenotype covariates.
DG	Directed graph of class QDG
QTLs	List of objects of class qtl.
addcov	Names of additive covariates. Must be valid phenotype names in cross. Expanded to include all intcov names.
intcov	Names of additive covariates. Must be valid phenotype names in cross.
x,object	Object of class qdg.perm.test.
...	Additional arguments ignored.

Details

qdg.perm.test performs nperm permutation-based test of LOD score for an edge of a directed graph.

Value

List composed by:

pvalue	Permutation p-value.
obs.lod	Observed LOD score.
PermSample	Permutation LOD scores sample.
node1	Character string with name of a phenotype nodes.
node2	Character string with name of a phenotype nodes.

References

Chaibub Neto et al. (2008) Inferring causal phenotype networks from segregating populations. Genetics 179: 1089-1100.

Examples

data(glxnet)
glxnet.cross <- calc.genoprob(glxnet.cross)
set.seed(1234)
glxnet.cross <- sim.geno(glxnet.cross)
## Should really use nperm = 1000 here.
qdg.perm.test(glxnet.cross, nperm = 10, "Glx", "Slc1a2",
   DG = glxnet.qdg$DG, QTLs = glxnet.qtl)

qtlnet documentation built on April 14, 2020, 6:24 p.m.