Description Usage Arguments Details Value Author(s) References See Also Examples
These routines work in conjunction with qb.hpdone,
qb.scantwo, qb.sliceone and
step.fitqtl to infer the number, pattern and position of
QTL from MCMC samples.
1 2 3 4 5 6 7 8 9 10 11 | qb.arch(object, ...)
## Default S3 method:
qb.arch(object, chr, pos, tolerance = 10, ...)
## S3 method for class 'step.fitqtl'
qb.arch(object, main, epistasis, ...)
## S3 method for class 'qb.BestPattern'
qb.arch(object, ...)
## S3 method for class 'qb.arch'
summary(object, ...)
## S3 method for class 'qb.arch'
print(x, ...)
|
object |
Object for appropriate method: summary of object of
class |
x |
Object of class |
chr |
Vector of chromosome numbers. |
pos |
Vector of positions on chromosomes (much be same length as |
tolerance |
Minimum distance for two QTL to be considered distinct. |
main |
Vector of chromosome identifiers with only main effects. |
epistasis |
Data frame with a 2-element vector of chromosome identifiers for each epistatic pair. |
... |
Not used here. |
Extract architecture in terms of chromosomes and positions of main QTL
and identifiers of epistatic pairs of QTL. The step.fitqtl
approach is used to compare an automatic fit to a user-defined set of
main chromosomes and epistatic pairs.
qtl |
Data frame with main QTL as |
by.num |
Data frame with epistatic pairs indexed by chromosome
number, labeled |
by.chr |
List with elements |
by.set |
List of connected sets of epistatic chromosomes. |
Brian S. Yandell
http://www.qtlbim.org
step.fitqtl, qb.sweave, qb.best
1 2 3 4 5 6 7 8 |
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