Effects names {noia} | R Documentation |
Provides and manipulates labels of geneticEffects.
effectsNamesGeneral(nloc = 2) effectsNamesMultilinear(nloc=2, max.level=2, max.dom=2) effectsSelect(nloc, max.level=NULL, max.dom=NULL) effNames(effects=NULL, loci=NULL, nloc=1)
nloc |
Number of loci. |
max.level |
Maximum level of interactions. |
max.dom |
Maximum level of dominance. |
effects |
Vector of characters. |
loci |
Vector of locus positions. |
The codes for geneticEffects are stored into a vector of length 4,
effectsNames
. The first element of the vector is the code for
the absence of effect (default: "."
). The three other elements are
respectively additive effects (default: "a"
) dominance effects
(default: "d"
), and multilinear epistatic effects (default:
"e"
).
The names of geneticEffects contains as many characters as the number of
loci in the system. The additive effect of the first locus in a 3-locus
system will be "a.."
, and the "Dominance by Dominance" between loci 2
and 4 in a 5-locus system will be ".d.d."
. Directionality of epistasis
between two (or more) loci is indicated by as many "e"
as necessary
(e.g. ".ee."
for the interaction between loci 2 and 3 in a 4-locus
case).
effectsNamesGeneral
and effectsNamesMultilinear
provide
a list of the names of the geneticEffects, in the right orders to be
processed by the NOIA framework (Alvarez-Castro and Carlborg 2007).
effNames
is a low-level routine, called by the other functions. It
provides names "on demand", for instance effNames(c("a","d"),c(2,4),5)
will generate ".a.d."
, i.e. an "a"
at locus 2 and a "d"
at locus 4, in a set of 5 loci.
Alvarez-Castro JM, Carlborg O. (2007). A unified model for functional and statistical epistasis and its application in quantitative trait loci analysis. Genetics 176(2):1151-1167.
Le Rouzic A, Alvarez-Castro JM. (2008). Estimation of genetic effects and genotype-phenotype maps. Evolutionary Bioinformatics, in press.
geneticEffects
, genNames
,
linearRegression
, multilinearRegression
.
effectsNamesGeneral(3) effectsSelect(nloc=3, max.level=1)