
lod(x, markers=1:x$nMark, t=c(0, 0.1, 0.2, 0.5), tol=0.01,
max.only=FALSE, silent=max.only)
linkdat
object.optimize
as its tolerance parameter.max.only=FALSE
, a linkres
object, essentially a matrix containing the LOD scores. If t
is numeric, the matrix has dimensions length(t), length(markers)
, and the entry in row t_0
, column m_0
is the lod score of the pedigree for marker m_0
assumming a recombination rate of t_0
.
If t="max"
, the linkres
matrix has one column per marker and two rows: The first containing the LOD score and the second the optimal recombination ratio for each marker.
If a marker has incompatible values (i.e. if a child of homozygous 1/1 parents has a 2 allele), the corresponding matrix entries are NaN.
If max.only=TRUE
, only the highest LOD score is returned, as a numeric of length 1.log_10(L(t) - log_10(L(t=0.5)
, where L(t)
denotes the likelihood of the observed pedigree data as a function of the recombination ratio t
between the marker and the disease locus.likelihood
, optimize
data(toyped)
x=linkdat(toyped, model=1)
lod(x, t=0)
data(largefam)
y=linkdat(largefam)
y=setModel(y, model=1, penetrances=c(.001, .9, .99))
lod(y, markers=305:310)
lod(y, markers=305:310, t="max")
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