Multilinear tools {noia}R Documentation

Tools for the Multilinear Regression

Description

The functions perform various tasks required by the multilinear regression model.

Usage

formulaMultilinear(nloc = 2, max.level = 2, max.dom = 2, e.unique=FALSE)
reconstructLinearEffects(noia.multilinear)
startingValues(reg, max.level = 2, max.dom = 2, e.unique = FALSE)
startingValuesLinear(noia.linear, max.level = 2, max.dom = 2, e.unique = FALSE)
startingValuesMultilinear(noia.multilinear, max.level = 2, max.dom = 2, e.unique = FALSE)

Arguments

nloc Number of loci.
max.level Maximum level of interactions.
max.dom Maximum level for dominance.
noia.multilinear Object of class "noia.multilinear" provided by multilinearRegression.
noia.linear Object of class "noia.linear" provided by linearRegression.
reg Object of class "noia.linear" or "noia.multilinear".
e.unique Whether a single interaction term is used for all pairs.

Details

Because of the way the non-linearRegression function nls works, the multilinear formula has to follow a specific form, with specific names for parameters. formulaMultilinear provides this formula.

reconstructLinearEffects generates a vector of geneticEffects, including general interaction effects (e.g. Additive by Additive etc) from the result of a multilinearRegression. This is necessary for further computation of the Genotype-to-Phenotype map.

Finally, startingValues provide a vector of starting values for the multilinear regression, from the result of a linear regression (through the function startingValuesLinear) or a simplier multilinear regression (through StartingValuesMultilinear). Such starting values are necessary to ensure the convergence of the non-linearRegression (nls).

Author(s)

Arnaud Le Rouzic <a.p.s.lerouzic@bio.uio.no>

References

Hansen TF, Wagner G. (2001) Modeling genetic architecture: A multilinear theory of gene interactions. Theoretical Population Biology 59:61-86.

Le Rouzic A, Alvarez-Castro JM. (2008). Estimation of genetic effects and genotype-phenotype maps. Evolutionary Bioinformatics, in press.

See Also

multilinearRegression, GPmap.

Examples

set.seed(123456789)

map <- c(0.25, -0.75, -0.75, -0.75, 2.25, 2.25, -0.75, 2.25, 2.25)
pop <- simulatePop(map, N=500, sigmaE=0.2, type="F2")

linear <- linearRegression(phen=pop$phen, gen=pop[2:3])
multilinear <- multilinearRegression(phen=pop$phen, 
        gen=cbind(pop$Loc1, pop$Loc2))

formulaMultilinear(nloc=2)
startingValues(linear)
reconstructLinearEffects(multilinear)

[Package noia version 0.91 Index]