Multilinear tools {noia} | R Documentation |
The functions perform various tasks required by the multilinear regression model.
formulaMultilinear(nloc=2, max.level=2, max.dom=2, e.unique=FALSE) reconstructLinearEffects(noia.multilinear) startingValues(phen, genZ, reference="noia", max.level=2, max.dom=2, fast=FALSE, e.unique=FALSE, start.algo="linear", bilinear.steps=NULL, ...) startingValuesReg(reg, max.level=2, max.dom=2, e.unique=FALSE, nloc=NULL) startingValuesNothing(nloc, max.level=2, max.dom=2, e.unique=FALSE) startingValuesLinear(noia.linear, max.level=2, max.dom=2, e.unique=FALSE, e.init=TRUE) startingValuesMultilinear(noia.multilinear, max.level=2, max.dom=2, e.unique=FALSE) bilinearStep(form, X, phen, marginal, interactions, ...)
nloc |
Number of loci. |
max.level |
Maximum order of interactions. |
max.dom |
Maximum order for dominance. |
e.unique |
Whether a single interaction term is used for all pairs. |
e.init |
Whether starting values for epistatic effects should be calculated (if FALSE , all epistatic effects are set to 0). |
phen |
Vector of phenotypes. |
genZ |
The matrix of individual genotypic probabilities in the population. |
reference |
The reference point from which the regression is performed. |
fast |
Use of the "fast" algorithm. |
start.algo |
Algorithm used to compute the starting values. Can be "linear" , "multilinear" , "subset" or "bilinear" . |
bilinear.steps |
Number of calls of the bilinearStep function. Ignored if start.algo is not "bilinear" . |
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" . |
form |
A multilinear formula as returned by formulaMultilinear . |
X |
The product between a Z matrix and a S matrix. Can be provided directly by genZ2X . |
marginal |
A list of marginal effects. |
interactions |
A list of interaction effects. Should be complementary to marginal , together accounting for all genetic effects. |
... |
Extra parameters to the non-linear regression function nls . |
Because of the way the non-linear regression 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 genetic effects,
including general interaction effects (e.g. Additive by Additive etc) from
the result of a multilinear regression. 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
). startingValuesNothing
generates a list of starting values and sets all of them to 0. Starting
values are necessary to ensure the convergence of the non-linearRegression
(nls
).
Arnaud Le Rouzic <a.p.s.lerouzic@bio.uio.no>
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, 4.
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) startingValuesReg(linear) reconstructLinearEffects(multilinear)