@hugov/correl-range2 v0.2.1

correl-range2

correlated variable monte carlo simulations

• Example • API • Notes • License

Example

import SIM from '../sim.js'

const res = SIM(
(_,
// initiation ran once
fixed$ = _`600_000 900_000 [0 demand:0.6 price:0.3`,
month$ = _`5,000 7,000 demand:0.5 season:0.5`,
months = _`6 9 [1 season:0.5 price:-0.5`
)=>(
// calculations on every iterations
total$ = fixed$ + month$ * months
)=>({
// exported results
months,
month$,
total$
})
).run(10_000)

//console.log(res.buffer)
const stats=res.stats
console.log('total$ range', stats.total$.Q(0.1).toFixed(0), stats.total$.Q(0.9).toFixed(0))
console.log('correlation', stats.total$.cor('months'))

API

sim( factory, {confidence=0.8, resolution=128} ).run( N=25_000 ) ⇒ simulation

• factory: randomVariableFactory => model
• randomVariableFactory: taggedTemplatelow high [min med max] {riskName:40%}, ...correlation) => randomVariable to match the simulation confidence interval. The string is parsed to match the metanorm arguments
• randomVariable: with .valueOf() that changes on each iteration
• simulation
  • stats: empirical distribution cdf, pdf, quantiles, average (based on modules sample-distribution and lazy-stats)

Notes

1. use case is human approximation in decision making - "guesstimates"
2. default is to use a confidence interval of 80%
3. variables can be correlated with independent risk factors by providing the linear factor
4. to maintain correlation, each variable returns a single value per cycle - random variables are constant within a given cycle

License

MIT © Hugo Villeneuve