Agricultural economics · Thünen Institute of Farm Economics / University Bonn
d3.mean(data.map(d => d.y))
function randomTruncSkewNormal({
rng = Math.random,
range = [-Infinity, Infinity],
mean,
stdDev,
skew = 0
}) {
// Before we start, we need to make sure the mean value is actually
// within our desired range
if (mean < range[0] || mean > range[1]) {
throw Error(`Mean of ${mean} not in desired range of ${range}!`);
}
// Box-Muller transform
function randomNormals(rng) {
let u1 = 0,
u2 = 0;
//Convert [0,1) to (0,1)
while (u1 === 0) u1 = rng();
while (u2 === 0) u2 = rng();
const R = Math.sqrt(-2.0 * Math.log(u1));
const Θ = 2.0 * Math.PI * u2;
return [R * Math.cos(Θ), R * Math.sin(Θ)];
}
// Skew-normal transform
// If a variate is either below or above the desired range,
// we recursively call the randomSkewNormal function until
// a value within the desired range is drawn
function randomSkewNormal(rng, mean, stdDev, skew = 0) {
const [u0, v] = randomNormals(rng);
if (skew === 0) {
const value = mean + stdDev * u0;
if (value < range[0] || value > range[1])
return randomSkewNormal(rng, mean, stdDev, skew);
return value;
}
const sig = skew / Math.sqrt(1 + skew * skew);
const u1 = sig * u0 + Math.sqrt(1 - sig * sig) * v;
const z = u0 >= 0 ? u1 : -u1;
const value = mean + stdDev * z;
if (value < range[0] || value > range[1])
return randomSkewNormal(rng, mean, stdDev, skew);
return value;
}
return randomSkewNormal(rng, mean, stdDev, skew);
}
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