View source: R/summary.scantwo.R
max.scantwo | R Documentation |
Print the pair of loci with the largest LOD score in the results of
scantwo
.
## S3 method for class 'scantwo'
max(object, lodcolumn=1,
what=c("best", "full", "add", "int"),
na.rm=TRUE, ...)
object |
An object of class |
lodcolumn |
If the scantwo results contain LOD scores for multiple phenotypes, this argument indicates which to use. |
what |
Indicates for which LOD score the maximum should be reported. |
na.rm |
Ignored. |
... |
Ignored. |
This is very similar to the summary.scantwo
function, though this pulls out one pair of positions.
If what="best"
, we find the pair of positions at which the LOD
score for the full model (2 QTL + interaction) is maximized, and then
also print the positions on that same pair of chromosomes at which the
additive LOD score is maximized.
In the other cases, we pull out the pair of positions with the largest
LOD score; which LOD score is considered is indicated by the
what
argument.
An object of class summary.scantwo
, to be printed by
print.summary.scantwo
, with the pair of positions with the
maximum LOD score. (Which LOD score is considered is indicated by the
what
argument.)
Note that, for output from addpair
in which the
new loci are indicated explicitly in the formula, the summary provided
by max.scantwo
is somewhat special.
All arguments (except, of course, the input
object
) are ignored.
If the formula is symmetric in the two new QTL, the output has just two LOD
score columns: lod.2v0
comparing the full model to the model
with neither of the new QTL, and lod.2v1
comparing the full
model to the model with just one new QTL.
If the formula is not symmetric in the two new QTL, the output
has three LOD score columns: lod.2v0
comparing the full model
to the model with neither of the new QTL, lod.2v1b
comparing
the full model to the model in which the first of the new QTL is
omitted, and lod.2v1a
comparing the full model to the model
with the second of the new QTL omitted.
Karl W Broman, broman@wisc.edu
scantwo
, plot.scantwo
,
summary.scantwo
data(fake.f2)
fake.f2 <- calc.genoprob(fake.f2, step=10)
out.2dim <- scantwo(fake.f2, method="hk")
max(out.2dim)
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