Published
Edited
May 5, 2021
dataMasterFraction = {
const data = dataErrorCatastrophe.filter(({ distance }) => distance === 0).map(d => {
const pop = d.pop / d.popTotal
return {
mu: d.mu,
oneMinusQ: 1 - (1 - d.mu) ** L,
pop,
logPop: Math.log(pop),
}
})
return data
}
dataMasterFractionByQ = {
const data = []
for (let Q = 1; Q >= 0.9; Q -= 0.005) {
// Q = (1 - mu) ** L
// Q ** (1/L) = 1 - mu
// mu = 1 - Q ** (1/L)
const mu = 1 - Q ** (1/L)
const W = getQuasiSpeciesEvolutionMatrix(mu)
const equilibriumPop = getLeadingEigenvector(W)
const popTotal = d3.sum(equilibriumPop)
data.push({
oneMinusQ: 1 - Q,
mu,
pop: equilibriumPop[0],
logPop: Math.log(equilibriumPop[0] / popTotal)
})
}
return data
}
dataErrorCatastrophe = {
const data = []
for (let mu = 0; mu < 0.1; mu += 0.001) {
const W = getQuasiSpeciesEvolutionMatrix(mu)
const equilibriumPop = getLeadingEigenvector(W)
const popTotal = d3.sum(equilibriumPop)
equilibriumPop.forEach((pop, distance) => {
data.push({
mu,
pop,
distance,
popTotal,
})
})
}
return data
}
mu_c = selectionCoeff / L
Q_c = 1 / selectionCoeff
Q = (1 - mutationRate) ** L
equilibrium = normalize(vectors.getColumn(maxEigenValueIndex))
vectors = e.eigenvectorMatrix;
maxEigenValueIndex = d3.maxIndex(eigenValues, d => Math.abs(d))
eigenValues = e.realEigenvalues;
e = new mlm.EigenvalueDecomposition(W);
W = getQuasiSpeciesEvolutionMatrix()
function getQuasiSpeciesEvolutionMatrix(mu = mutationRate) {
const s = selectionCoeff
const Q = (1 - mu) ** L
return new mlm.Matrix([
[s*Q, 3*s*mu, 0, 0],
[ mu, Q, 2*mu, 0],
[ 0, 2*mu, Q, mu],
[ 0, 0, 3*mu, Q]
]);
}
function getLeadingEigenvector(M) {
const e = new mlm.EigenvalueDecomposition(M)
const eigenValues = e.realEigenvalues
const maxEigenValueIndex = d3.maxIndex(eigenValues, d => Math.abs(d))
const vectors = e.eigenvectorMatrix
// return vectors.getColumn(maxEigenValueIndex)
return normalize(vectors.getColumn(maxEigenValueIndex))
}
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