Kolmogorov-Smirnov Goodness-of-Fit Test

One-sample Kolmogorov-Smirnov goodness-of-fit test.

Usage

var kstest = require( '@stdlib/math/stats/kstest' );

kstest( x, y[, ...params][, opts] )

For a numeric array or typed array x, a Kolmogorov-Smirnov goodness-of-fit is computed for the null hypothesis that the values of x come from the distribution specified by y. y can be either a string with the name of the distribution to test against, or a function. In the latter case, y is expected to be the cumulative distribution function (CDF) of the distribution to test against, with its first parameter being the value at which to evaluate the CDF and the remaining parameters constituting the parameters of the distribution. The parameters of the distribution are passed as additional arguments after y from kstest to the chosen CDF. The function returns an object holding the calculated test statistic statistic and the pValue of the test.

var factory = require( '@stdlib/random/base/uniform' ).factory;
var runif;
var out;
var x;
var i;

runif = factory( 0.0, 1.0, {
    'seed': 8798
});

x = new Array( 100 );
for ( i = 0; i < x.length; i++ ) {
    x[ i ] = runif();
}
out = kstest( x, 'uniform', 0.0, 1.0 );
// returns { 'pValue': ~0.703, 'statistic': ~0.069, ... }

The returned object comes with a .print() method which when invoked will print a formatted output of the hypothesis test results.

console.log( out.print() );
/* e.g., =>
    Kolmogorov-Smirnov goodness-of-fit test.

    Null hypothesis: the CDF of `x` is equal equal to the reference CDF.

        pValue: 0.7039
        statistic: 0.0689

    Test Decision: Fail to reject null in favor of alternative at 5% significance level
*/

The function accepts the following options:

  • alpha: number in the interval [0,1] giving the significance level of the hypothesis test. Default: 0.05.
  • alternative: Either two-sided, less or greater. Indicates whether the alternative hypothesis is that the true distribution of x is not equal to the reference distribution specified by y (two-sided), whether it is less than the reference distribution or greater than the reference distribution. Default: two-sided.
  • sorted: boolean indicating if the x array is in sorted order (ascending). Default: false.

By default, the test is carried out at a significance level of 0.05. To choose a custom significance level, set the alpha option.

out = kstest( x, 'uniform', 0.0, 1.0, {
    'alpha': 0.1
});
console.log( out.print() );
/* e.g., =>
    Kolmogorov-Smirnov goodness-of-fit test.

    Null hypothesis: the CDF of `x` is equal equal to the reference CDF.

        pValue: 0.7039
        statistic: 0.0689

    Test Decision: Fail to reject null in favor of alternative at 10% significance level
*/

By default, the function tests the null hypothesis that the true distribution of x and the reference distribution y are equal to each other against the alternative that they are not equal. To carry out a one-sided hypothesis test, set the alternative option to either less or greater.

var factory = require( '@stdlib/random/base/uniform' ).factory;
var runif;
var out;
var x;
var i;

runif = factory( 0.0, 1.0, {
    'seed': 8798
});

x = new Array( 100 );
for ( i = 0; i < x.length; i++ ) {
    x[ i ] = runif();
}

out = kstest( x, 'uniform', 0.0, 1.0, {
    'alternative': 'less'
});
// returns { 'pValue': ~0.358, 'statistic': ~0.07, ... }

out = kstest( x, 'uniform', 0.0, 1.0, {
    'alternative': 'greater'
});
// returns { 'pValue': ~0.907, 'statistic': ~0.02, ... }

To perform the Kolmogorov-Smirnov test, the data has to be sorted in ascending order. If the data in x are already sorted, set the sorted option to true to speed up computation.

x = [ 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 ];

out = kstest( x, 'uniform', 0.0, 1.0, {
    'sorted': true
});
// returns { 'pValue': ~1, 'statistic': 0.1, ... }

Examples

var kstest = require( '@stdlib/math/stats/kstest' );
var rnorm = require( '@stdlib/random/base/normal' ).factory({
    'seed': 4839
});

var table;
var out;
var i;
var x;

// Values drawn from a Normal(3,1) distribution
x = new Array( 100 );
for ( i = 0; i < 100; i++ ) {
    x[ i ] = rnorm( 3.0, 1.0 );
}

// Test against N(0,1)
out = kstest( x, 'normal', 0.0, 1.0 );
table = out.print();
/* e.g., returns
    Kolmogorov-Smirnov goodness-of-fit test.

    Null hypothesis: the CDF of `x` is equal to the reference CDF.

        statistic: 0.847
        pValue: 0

    Test Decision: Reject null in favor of alternative at 5% significance level
*/

// Test against N(3,1)
out = kstest( x, 'normal', 3.0, 1.0 );
table = out.print();
/* e.g., returns
    Kolmogorov-Smirnov goodness-of-fit test.

    Null hypothesis: the CDF of `x` is equal to the reference CDF.

        statistic: 0.0733
        pValue: 0.6282

    Test Decision: Fail to reject null in favor of alternative at 5% significance level
*/