// NMB method to determine frontier roughly adapted from Suen and Goldhaber-Fiebert's article
// Admitly this may not be an optimal implementation
// I've preserved the variable names to match the original R source code for easy comparison
function frontierByNMB(data, maxWTP) {
const CEmat = data;
let WTPtestPoints = [0];
let ICERmat = CEmat.forEach((d, i) =>
CEmat.forEach((e, j) => {
if (j >= i) return; // avoid redundant calcs
let wtp = (d.cost - e.cost) / (d.benefit - e.benefit);
if (
wtp > 0 &&
isFinite(wtp) &&
WTPtestPoints.indexOf(wtp) < 0 && // unique
wtp <= maxWTP
) {
WTPtestPoints.push(wtp);
}
})
);
WTPtestPoints = WTPtestPoints.sort((a, b) => a - b);
let NMBmat = [];
CEmat.forEach(d =>
NMBmat.push(WTPtestPoints.map(e => e * d.benefit - d.cost))
);
let NMBmatTransposed = d3.transpose(NMBmat);
let maxVals = NMBmatTransposed.map(arr => d3.max(arr));
let indiciesOfMax = [];
NMBmatTransposed.forEach((arr, i) => {
arr.forEach((d, j) => {
if (d == maxVals[i] && indiciesOfMax.indexOf(j) < 0)
indiciesOfMax.push(j);
});
});
return indiciesOfMax;
}
// convex hull method to determine frontier
function frontierByHull(data, maxWTP) {
const points = data.map((d, i) => [d.cost, d.benefit, d.index]);
const initial_hull = d3.polygonHull(points);
// rehydrate point info into an object
let hull = initial_hull.map((d, i) => {
return _.assign({
cost: d[0],
benefit: d[1],
original_index: d[2],
hull_index: i,
on_frontier: false
});
});
// Slice the list of points so that we start with the point with the smallest cost
// e.g. if array = [1,2,3,4,5,6] and min_cost_index is 3 --> [3,4,5,6,1,2]
const min_cost_index = _.minBy(hull, 'cost').hull_index;
hull = hull.slice(min_cost_index).concat(hull.slice(0, min_cost_index));
// Iterate through the points on the hull in order
// Stop if wtp is > maxWTP or if benefit is less than benefit of previous point
let previous_benefit = -Infinity;
let previous_cost = 0;
hull.forEach(d => {
let wtp = (d.cost - previous_cost) / (d.benefit - previous_benefit);
if (d.benefit >= previous_benefit && wtp <= maxWTP) {
d.on_frontier = true;
previous_cost = d.cost;
previous_benefit = d.benefit;
}
});
// return original point index for each point on the frontier
return hull.filter(d => d.on_frontier).map(d => d.original_index);
}
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.