@stdlib/stats-base-dists-uniform-quantile NPM

Quantile Function

Uniform distribution quantile function.

The quantile function for a continuous uniform random variable is

for 0 <= p <= 1, where a is the minimum support and b is the maximum support. The parameters must satisfy a < b.

Installation

npm install @stdlib/stats-base-dists-uniform-quantile

Usage

var quantile = require( '@stdlib/stats-base-dists-uniform-quantile' );

quantile( p, a, b )

Evaluates the quantile function for a continuous uniform distribution with parameters a (minimum support) and b (maximum support).

var y = quantile( 0.8, 0.0, 1.0 );
// returns 0.8

y = quantile( 0.5, 0.0, 10.0 );
// returns 5.0

If provided a probability p outside the interval [0,1], the function returns NaN.

var y = quantile( 1.9, 0.0, 1.0 );
// returns NaN

y = quantile( -0.1, 0.0, 1.0 );
// returns NaN

If provided NaN as any argument, the function returns NaN.

var y = quantile( NaN, 0.0, 1.0 );
// returns NaN

y = quantile( 0.0, NaN, 1.0 );
// returns NaN

y = quantile( 0.0, 0.0, NaN );
// returns NaN

If provided a >= b, the function returns NaN.

var y = quantile( 0.4, 2.0, 1.0 );
// returns NaN

quantile.factory( a, b )

Returns a function for evaluating the quantile function of a continuous uniform distribution with parameters a (minimum support) and b (maximum support).

var myquantile = quantile.factory( 0.0, 4.0 );
y = myquantile( 0.8 );
// returns 3.2

Examples

var randu = require( '@stdlib/random-base-randu' );
var quantile = require( '@stdlib/stats-base-dists-uniform-quantile' );

var a;
var b;
var p;
var y;
var i;

for ( i = 0; i < 25; i++ ) {
    p = randu();
    a = (randu() * 20.0) - 20.0;
    b = a + (randu() * 40.0);
    y = quantile( p, a, b );
    console.log( 'p: %d, a: %d, b: %d, Q(p;a,b): %d', p.toFixed( 4 ), a.toFixed( 4 ), b.toFixed( 4 ), y.toFixed( 4 ) );
}

Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.