qdg.perm.test {qtlnet}

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

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)