covar {bqtl} | R Documentation |
Treat locus as covariate
Description
Sometimes it is helps speed computations to linearize the likelihood
or at least a part of it w.r.t. the locus allele values. Both
'Haley-Knott regression' and 'composite interval mapping' use this
approach. covar
provides a mechanism for creating formula
objects that specify such linearizations.
Usage
covar(x)
Arguments
x |
The name of a locus (except for F2 designs, when it is the
name of an effect like 'add.m.32') or any argument of the sort that
|
Details
The function covar
actually only returns x
. The real
work is done by a covar
function that is hidden inside of
bqtl
, where the arguments are parsed as for locus
. Each
of the return values from locus
is prefixed by "covar(" and
suffixed by ")". If x
is a name of a locus or effect, then
paste("covar(",deparse(x),")")
is returned. Later, when
bqtl
calls lapadj
, terms like covar(PVV4.1)
are
recognized as requiring a linearization w.r.t. effect 'PVV4.1'.
Value
a character string or vector
Author(s)
Charles C. Berry cberry@ucsd.edu
References
HALEY, C. S. and S. A. KNOTT, 1992 A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315-324.
Knapp SJ, Bridges WC, and Birkes D. Mapping quantitative trait loci using molecular marker linkage maps. Theoretical and Applied Genetics 79: 583-592, 1990.
ZENG, Z.-B., 1994 Precision mapping of quantitative trait loci. Genetics 136:1457-1468