Median

Weibull distribution median.

The median for a Weibull random variable is

where lambda > 0 is the scale parameter and k > 0 is the shape parameter.

Usage

var median = require( '@stdlib/stats/base/dists/weibull/median' );

median( k, lambda )

Returns the median of a Weibull distribution with parameters k (shape parameter) and lambda (scale parameter).

var v = median( 1.0, 1.0 );
// returns ~0.693

v = median( 4.0, 12.0 );
// returns ~10.949

v = median( 8.0, 2.0 );
// returns ~1.91

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

var v = median( NaN, 2.0 );
// returns NaN

v = median( 2.0, NaN );
// returns NaN

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

var v = median( 0.0, 1.0 );
// returns NaN

v = median( -1.0, 1.0 );
// returns NaN

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

var v = median( 1.0, 0.0 );
// returns NaN

v = median( 1.0, -1.0 );
// returns NaN

Examples

var randu = require( '@stdlib/random/base/randu' );
var EPS = require( '@stdlib/constants/float64/eps' );
var median = require( '@stdlib/stats/base/dists/weibull/median' );

var lambda;
var k;
var v;
var i;

for ( i = 0; i < 10; i++ ) {
    k = ( randu()*10.0 ) + EPS;
    lambda = ( randu()*10.0 ) + EPS;
    v = median( k, lambda );
    console.log( 'k: %d, λ: %d, Median(X;k,λ): %d', k.toFixed( 4 ), lambda.toFixed( 4 ), v.toFixed( 4 ) );
}

C APIs

Usage

#include "stdlib/stats/base/dists/weibull/median.h"

stdlib_base_dists_weibull_median( k, lambda )

Returns the median of a Weibull distribution.

double out = stdlib_base_dists_weibull_median( 1.0, 1.0 );
// returns ~0.693

The function accepts the following arguments:

k: [in] double shape parameter.
lambda: [in] double scale parameter.

double stdlib_base_dists_weibull_median( const double k, const double lambda );

Examples

#include "stdlib/stats/base/dists/weibull/median.h"
#include <stdlib.h>
#include <stdio.h>

static double random_uniform( const double min, const double max ) {
    double v = (double)rand() / ( (double)RAND_MAX + 1.0 );
    return min + ( v*(max-min) );
}

int main( void ) {
    double lambda;
    double k;
    double y;
    int i;

    for ( i = 0; i < 25; i++ ) {
        lambda = random_uniform( 0.0, 10.0 );
        k = random_uniform( 0.0, 10.0 );
        y = stdlib_base_dists_weibull_median( k, lambda );
        printf( "k: %lf, λ: %lf, Median(x;k,λ): %lf\n", k, lambda, y );
    }
}