| NOIA matrix manipulation {noia} | R Documentation |
These functions perform the matrix computation required for the computation of geneticEffects and Genotype-to-Phenotype mapping.
gen2Z(gen) gen2genZ(gen) genZ2freq(genZ) genZ2S(type="F2", genZ=NULL, nloc=NULL) genZ2Z(genZ) Z2freq(Z) Sloc(type="F2", i=NULL, genZ=NULL)
gen |
The matrix of genotypes, one column per locus, the genotype is
coded 1, 2, 3. Missing data are allowed. |
genZ |
The matrix of genotypic probabilities, 3 columns per locus (one for the probability of each genotype). The sum of probabilities must be 1, and missing data are not allowed. |
type |
The type of the population. "F2", "F1",
"Finf". "P1", "P2, "G2A", "UWR" and
"noia" are possible. Default is "F2". |
nloc |
Number of loci. |
Z |
A matrix reflecting the genotype of the corresponding observed phenotypes, as defined in Alvarez-Castro and Carlborg 2007. |
i |
Index of the locus. |
gen2Zgen data set into a Z matrix that
is the data matrix in the regression. The function actually calls sequencially
gen2genZ and genZ2Z. gen2genZgen matrix into a genZ matrix. genZ2freqgenZ2SS matrix (see Alvarez-Castro and Carlborg
2007) for a given reference point. Some reference points are genotypic
frequency-dependent ("G2A" and "noia"), and the genZ
matrix must be provided. For the others, only the number of loci is
necessary. genZ2ZZ matrix from the genotypic
probabilities. See Alvarez-Castro and Carlborg 2007 for more details. Z2freqSlocS matrix, corresponding to one locus.
Frequency-dependent reference points will require the genZ matrix
and the index of the locus. Arnaud Le Rouzic <a.p.s.lerouzic@bio.uio.no>
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.
linearRegression, multilinearRegression
set.seed(123456789) map <- c(0.25, -0.75, -0.75, -0.75, 2.25, 2.25, -0.75, 2.25, 2.25) names(map) <- genNames(2) pop <- simulatePop(map, N=500, sigmaE=0.2, type="F2") gen <- pop[2:3] genZ <- gen2genZ(gen) Z <- genZ2Z(genZ)