nanvariancepn

Calculate the variance of an array ignoring NaN values and using a two-pass algorithm.

The population variance of a finite size population of size N is given by

where the population mean is given by

Often in the analysis of data, the true population variance is not known a priori and must be estimated from a sample drawn from the population distribution. If one attempts to use the formula for the population variance, the result is biased and yields a biased sample variance. To compute an unbiased sample variance for a sample of size n,

where the sample mean is given by

The use of the term n-1 is commonly referred to as Bessel's correction. Note, however, that applying Bessel's correction can increase the mean squared error between the sample variance and population variance. Depending on the characteristics of the population distribution, other correction factors (e.g., n-1.5, n+1, etc) can yield better estimators.

Installation

npm install @stdlib/stats-array-nanvariancepn

Usage

var nanvariancepn = require( '@stdlib/stats-array-nanvariancepn' );

nanvariancepn( x[, correction] )

Computes the variance of an array ignoring NaN values and using a two-pass algorithm.

var x = [ 1.0, -2.0, NaN, 2.0 ];

var v = nanvariancepn( x );
// returns ~4.3333

The function has the following parameters:

• x: input array.
• correction: degrees of freedom adjustment. Setting this parameter to a value other than 0 has the effect of adjusting the divisor during the calculation of the variance according to N-c where N corresponds to the number of non-NaN array elements and c corresponds to the provided degrees of freedom adjustment. When computing the variance of a population, setting this parameter to 0 is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the unbiased sample variance, setting this parameter to 1 is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction). Default: 1.0.

By default, the function computes the sample variance. To adjust the degrees of freedom when computing the variance, provide a correction argument.

var x = [ 1.0, -2.0, NaN, 2.0 ];

var v = nanvariancepn( x, 0.0 );
// returns ~2.8889

Notes

• If provided an empty array, the function returns NaN.
• If N - c is less than or equal to 0 (where c corresponds to the provided degrees of freedom adjustment and N corresponds to the number of non-NaN array elements), the function returns NaN.
• The function supports array-like objects having getter and setter accessors for array element access (e.g., @stdlib/array-base/accessor).

Examples

var uniform = require( '@stdlib/random-base-uniform' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var nanvariancepn = require( '@stdlib/stats-array-nanvariancepn' );

function rand() {
    if ( bernoulli( 0.8 ) < 1 ) {
        return NaN;
    }
    return uniform( -50.0, 50.0 );
}

var x = filledarrayBy( 10, 'generic', rand );
console.log( x );

var v = nanvariancepn( x );
console.log( v );

References

• Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." Communications of the ACM 9 (7). Association for Computing Machinery: 496–99. doi:10.1145/365719.365958.
• Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In Proceedings of the 30th International Conference on Scientific and Statistical Database Management. New York, NY, USA: Association for Computing Machinery. doi:10.1145/3221269.3223036.

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.

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License

See LICENSE.

Copyright

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