@stdlib/stats-base-snanvariancepn
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    snanvariancepn

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    Calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a two-pass algorithm.

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

    Equation for the population variance.

    where the population mean is given by

    Equation for the population mean.

    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,

    Equation for computing an unbiased sample variance.

    where the sample mean is given by

    Equation for the sample mean.

    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-base-snanvariancepn

    Usage

    var snanvariancepn = require( '@stdlib/stats-base-snanvariancepn' );

    snanvariancepn( N, correction, x, stride )

    Computes the variance of a single-precision floating-point strided array x ignoring NaN values and using a two-pass algorithm.

    var Float32Array = require( '@stdlib/array-float32' );
    
    var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
    
    var v = snanvariancepn( x.length, 1, x, 1 );
    // returns ~4.3333

    The function has the following parameters:

    • N: number of indexed elements.
    • 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 c corresponds to the provided degrees of freedom adjustment and n corresponds to the number of non-NaN indexed elements. 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).
    • x: input Float32Array.
    • stride: index increment for x.

    The N and stride parameters determine which elements in x are accessed at runtime. For example, to compute the variance of every other element in x,

    var Float32Array = require( '@stdlib/array-float32' );
    var floor = require( '@stdlib/math-base-special-floor' );
    
    var x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ] );
    var N = floor( x.length / 2 );
    
    var v = snanvariancepn( N, 1, x, 2 );
    // returns 6.25

    Note that indexing is relative to the first index. To introduce an offset, use typed array views.

    var Float32Array = require( '@stdlib/array-float32' );
    var floor = require( '@stdlib/math-base-special-floor' );
    
    var x0 = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
    var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
    
    var N = floor( x0.length / 2 );
    
    var v = snanvariancepn( N, 1, x1, 2 );
    // returns 6.25

    snanvariancepn.ndarray( N, correction, x, stride, offset )

    Computes the variance of a single-precision floating-point strided array ignoring NaN values and using a two-pass algorithm and alternative indexing semantics.

    var Float32Array = require( '@stdlib/array-float32' );
    
    var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
    
    var v = snanvariancepn.ndarray( x.length, 1, x, 1, 0 );
    // returns ~4.33333

    The function has the following additional parameters:

    • offset: starting index for x.

    While typed array views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the variance for every other value in x starting from the second value

    var Float32Array = require( '@stdlib/array-float32' );
    var floor = require( '@stdlib/math-base-special-floor' );
    
    var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
    var N = floor( x.length / 2 );
    
    var v = snanvariancepn.ndarray( N, 1, x, 2, 1 );
    // returns 6.25

    Notes

    • If N <= 0, both functions return 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 indexed elements), both functions return NaN.

    Examples

    var randu = require( '@stdlib/random-base-randu' );
    var round = require( '@stdlib/math-base-special-round' );
    var Float32Array = require( '@stdlib/array-float32' );
    var snanvariancepn = require( '@stdlib/stats-base-snanvariancepn' );
    
    var x;
    var i;
    
    x = new Float32Array( 10 );
    for ( i = 0; i < x.length; i++ ) {
        x[ i ] = round( (randu()*100.0) - 50.0 );
    }
    console.log( x );
    
    var v = snanvariancepn( x.length, 1, x, 1 );
    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

    Copyright © 2016-2021. The Stdlib Authors.

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