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{{alias}}( x, y[, options] )
Locally-weighted polynomial regression via the LOWESS algorithm.
Parameters
----------
x: Array<number>
x-axis values (abscissa values).
y: Array<number>
Corresponding y-axis values (ordinate values).
options: Object (optional)
Function options.
options.f: number (optional)
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`.
options.nsteps: number (optional)
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`.
options.delta: number (optional)
Nonnegative number which may be used to reduce the number of
computations. Default: 1/100th of the range of `x`.
options.sorted: boolean (optional)
Boolean indicating if the input array `x` is sorted. Default: `false`.
Returns
-------
out: Object
Object with ordered x-values and fitted values.
Examples
--------
> var x = new {{alias:@stdlib/array/float64}}( 100 );
> var y = new {{alias:@stdlib/array/float64}}( x.length );
> for ( var i = 0; i < x.length; i++ ) {
... x[ i ] = i;
... y[ i ] = ( 0.5*i ) + ( 10.0*{{alias:@stdlib/random/base/randn}}() );
... }
> var out = {{alias}}( x, y );
> var yhat = out.y;
> var h = {{alias:@stdlib/plot/ctor}}( [ x, x ], [ y, yhat ] );
> h.lineStyle = [ 'none', '-' ];
> h.symbols = [ 'closed-circle', 'none' ];
> h.view( 'window' );
See Also
--------
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