Quantile Function

Inverse gamma distribution quantile function.

The quantile function for an inverse gamma random variable is

for 0 <= p < 1, where alpha > 0 is the shape parameter and beta > 0 is the scale parameter.

Installation

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

Usage

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

quantile( p, alpha, beta )

Evaluates the quantile function for an inverse gamma distribution with parameters alpha (shape parameter) and beta (rate parameter).

var y = quantile( 0.8, 2.0, 1.0 );
// returns ~1.213

y = quantile( 0.5, 4.0, 2.0 );
// returns ~0.545

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

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

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

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

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

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

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

If provided alpha <= 0, the function returns NaN.

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

If provided beta <= 0, the function returns NaN.

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

quantile.factory( alpha, beta )

Returns a function for evaluating the quantile function of an inverse gamma distribution with parameters alpha (shape parameter) and beta (rate parameter).

var myquantile = quantile.factory( 2.0, 2.0 );
var y = myquantile( 0.8 );
// returns ~2.426

y = myquantile( 0.4 );
// returns ~0.989

Examples

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

var alpha;
var beta;
var p;
var y;
var i;

for ( i = 0; i < 10; i++ ) {
    p = randu();
    alpha = randu() * 5.0;
    beta = randu() * 5.0;
    y = quantile( p, alpha, beta );
    console.log( 'p: %d, α: %d, β: %d, Q(p;α,β): %d', p.toFixed( 4 ), alpha.toFixed( 4 ), beta.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.

Community

License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.