stdlib-js / stats-lowess

Locally-weighted polynomial regression via the LOWESS algorithm.

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LOWESS

Locally-weighted polynomial regression via the LOWESS algorithm.

Installation

npm install @stdlib/stats-lowess

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

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

lowess( x, y[, opts] )

For input arrays and/or typed arrays x and y, the function returns an object holding the ordered input values x and smoothed values for y.

var x = [
    4, 4, 7, 7, 8, 9, 10, 10, 10, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14,
    14, 14, 14, 15, 15, 15, 16, 16, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 20,
    20, 20, 20, 20, 22, 23, 24, 24, 24, 24, 25
];
var y = [
    2, 10, 4, 22, 16, 10, 18, 26, 34, 17, 28, 14, 20, 24, 28, 26, 34, 34, 46,
    26, 36, 60, 80, 20, 26, 54, 32, 40, 32, 40, 50, 42, 56, 76, 84, 36, 46, 68,
    32, 48, 52, 56, 64, 66, 54, 70, 92, 93, 120, 85
];

var out = lowess( x, y );
/* returns
    {
        'x': [
            4,
            4,
            7,
            7,
            ...,
            24,
            24,
            24,
            25
        ],
        'y': [
            ~4.857,
            ~4.857,
            ~13.1037,
            ~13.1037,
            ...,
            ~79.102,
            ~79.102,
            ~79.102,
            ~84.825
        ]
    }
*/

The function accepts the following options:

• f: positive number specifying the smoothing span, i.e., the proportion of points which influence smoothing at each value. Larger values correspond to more smoothing. Default: 2/3.
• nsteps: number of iterations in the robust fit (fewer iterations translates to faster function execution). If set to zero, the nonrobust fit is returned. Default: 3.
• delta: nonnegative number which may be used to reduce the number of computations. Default: 1/100th of the range of x.
• sorted: boolean indicating if the input array x is sorted. Default: false.

By default, smoothing at each value is determined by 2/3 of all other points. To choose a different smoothing span, set the f option.

var x = [
    4, 4, 7, 7, 8, 9, 10, 10, 10, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14,
    14, 14, 14, 15, 15, 15, 16, 16, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 20,
    20, 20, 20, 20, 22, 23, 24, 24, 24, 24, 25
];
var y = [
    2, 10, 4, 22, 16, 10, 18, 26, 34, 17, 28, 14, 20, 24, 28, 26, 34, 34, 46,
    26, 36, 60, 80, 20, 26, 54, 32, 40, 32, 40, 50, 42, 56, 76, 84, 36, 46, 68,
    32, 48, 52, 56, 64, 66, 54, 70, 92, 93, 120, 85
];

var out = lowess( x, y, {
    'f': 0.2
});
/* returns
    {
        'x': [
            4,
            4,
            7,
            ...,
            24,
            24,
            25
        ],
        'y': [
            ~6.03,
            ~6.03,
            ~12.68,
            ...,
            ~82.575,
            ~82.575,
            ~93.028
        ]
    }
*/

By default, three iterations of locally weighted regression fits are calculated after the initial fit. To set a different number of iterations for the robust fit, set the nsteps option.

var x = [
    4, 4, 7, 7, 8, 9, 10, 10, 10, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14,
    14, 14, 14, 15, 15, 15, 16, 16, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 20,
    20, 20, 20, 20, 22, 23, 24, 24, 24, 24, 25
];
var y = [
    2, 10, 4, 22, 16, 10, 18, 26, 34, 17, 28, 14, 20, 24, 28, 26, 34, 34, 46,
    26, 36, 60, 80, 20, 26, 54, 32, 40, 32, 40, 50, 42, 56, 76, 84, 36, 46, 68,
    32, 48, 52, 56, 64, 66, 54, 70, 92, 93, 120, 85
];

var out = lowess( x, y, {
    'nsteps': 20
});
/* returns
    {
        'x': [
            4,
            ...,
            25
        ],
        'y': [
            ~4.857,
            ...,
            ~84.825
        ]
    }
*/

To save computations, set the delta option. For cases where one has a large number of (x,y)-pairs, carrying out regression calculations for all points is not likely to be necessary. By default, delta is set to 1/100th of the range of the values in x. In this case, if the values in x were uniformly scattered over the entire range, the locally weighted regression would be calculated at approximately 100 points. On the other hand, for small data sets with less than 100 observations, one can safely set the option to zero so calculations are made for each data point.

var x = [
    4, 4, 7, 7, 8, 9, 10, 10, 10, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14,
    14, 14, 14, 15, 15, 15, 16, 16, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 20,
    20, 20, 20, 20, 22, 23, 24, 24, 24, 24, 25
];
var y = [
    2, 10, 4, 22, 16, 10, 18, 26, 34, 17, 28, 14, 20, 24, 28, 26, 34, 34, 46,
    26, 36, 60, 80, 20, 26, 54, 32, 40, 32, 40, 50, 42, 56, 76, 84, 36, 46, 68,
    32, 48, 52, 56, 64, 66, 54, 70, 92, 93, 120, 85
];

var out = lowess( x, y, {
    'delta': 0.0
});
/* returns
    {
        'x': [
            4,
            ...,
            25
        ],
        'y': [
            ~4.857,
            ...,
            ~84.825
        ]
    }
*/

If the elements of x are sorted in ascending order, set the sorted option to true.

var x = [
    4, 4, 7, 7, 8, 9, 10, 10, 10, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14,
    14, 14, 14, 15, 15, 15, 16, 16, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 20,
    20, 20, 20, 20, 22, 23, 24, 24, 24, 24, 25
];
var y = [
    2, 10, 4, 22, 16, 10, 18, 26, 34, 17, 28, 14, 20, 24, 28, 26, 34, 34, 46,
    26, 36, 60, 80, 20, 26, 54, 32, 40, 32, 40, 50, 42, 56, 76, 84, 36, 46, 68,
    32, 48, 52, 56, 64, 66, 54, 70, 92, 93, 120, 85
];

var out = lowess( x, y, {
    'sorted': true
});
/* returns
    {
        'x': [
            4,
            ...,
            25
        ],
        'y': [
            ~4.857,
            ...,
            ~84.825
        ]
    }
*/

Examples

var randn = require( '@stdlib/random-base-randn' );
var Float64Array = require( '@stdlib/array-float64' );
var plot = require( '@stdlib/plot-ctor' );
var lowess = require( '@stdlib/stats-lowess' );

var x;
var y;
var i;

// Create some data...
x = new Float64Array( 100 );
y = new Float64Array( x.length );
for ( i = 0; i < x.length; i++ ) {
    x[ i ] = i;
    y[ i ] = ( 0.5*i ) + ( 10.0*randn() );
}

var opts = {
    'delta': 0
};

var out = lowess( x, y, opts );
var h = plot( [ x, out.x ], [ y, out.y ] );

h.lineStyle = [ 'none', '-' ];
h.symbols = [ 'closed-circle', 'none' ];

h.view( 'stdout' );

References

• Cleveland, William S. 1979. "Robust Locally and Smoothing Weighted Regression Scatterplots." Journal of the American Statistical Association 74 (368): 829–36. doi:10.1080/01621459.1979.10481038.
• Cleveland, William S. 1981. "Lowess: A program for smoothing scatterplots by robust locally weighted regression." American Statistician 35 (1): 54–55. doi:10.2307/2683591.

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Notice

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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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