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* @license Apache-2.0
*
* Copyright (c) 2025 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
'use strict';
// MODULES //
var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
var getStride = require( '@stdlib/ndarray/base/stride' );
var getOffset = require( '@stdlib/ndarray/base/offset' );
var getData = require( '@stdlib/ndarray/base/data-buffer' );
var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' );
var strided = require( '@stdlib/stats/strided/ztest' ).ndarray;
// MAIN //
/**
* Computes a one-sample Z-test for a one-dimensional ndarray.
*
* ## Notes
*
* - The function expects the following ndarrays in order:
*
* - a one-dimensional input ndarray.
* - a zero-dimensional output ndarray containing a results object.
* - a zero-dimensional ndarray specifying the alternative hypothesis.
* - a zero-dimensional ndarray specifying the significance level.
* - a zero-dimensional ndarray specifying the mean under the null hypothesis.
* - a zero-dimensional ndarray specifying the known standard deviation.
*
* @param {ArrayLikeObject<Object>} arrays - array-like object containing ndarrays
* @returns {ndarrayLike} output ndarray
*
* @example
* var Float64Results = require( '@stdlib/stats/base/ztest/one-sample/results/float64' );
* var resolveEnum = require( '@stdlib/stats/base/ztest/alternative-resolve-enum' );
* var structFactory = require( '@stdlib/array/struct-factory' );
* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
* var ndarray = require( '@stdlib/ndarray/ctor' );
*
* var opts = {
* 'dtype': 'generic'
* };
*
* // Define a one-dimensional input ndarray:
* var xbuf = [ 1.0, 3.0, 4.0, 2.0 ];
* var x = new ndarray( opts.dtype, xbuf, [ 4 ], [ 1 ], 0, 'row-major' );
*
* // Specify the alternative hypothesis:
* var alt = scalar2ndarray( resolveEnum( 'two-sided' ), {
* 'dtype': 'int8'
* });
*
* // Specify the significance level:
* var alpha = scalar2ndarray( 0.05, opts );
*
* // Specify the mean value under the null hypothesis:
* var mu = scalar2ndarray( 0.0, opts );
*
* // Specify the known standard deviation:
* var sigma = scalar2ndarray( 1.0, opts );
*
* // Create a zero-dimensional results ndarray:
* var ResultsArray = structFactory( Float64Results );
* var out = new ndarray( Float64Results, new ResultsArray( 1 ), [], [ 0 ], 0, 'row-major' );
*
* // Perform a Z-test:
* var v = ztest( [ x, out, alt, alpha, mu, sigma ] );
* // returns <ResultsArray>
*
* console.log( v.get().toString() );
*/
function ztest( arrays ) {
var alpha;
var sigma;
var alt;
var mu;
var x;
var y;
x = arrays[ 0 ];
y = ndarraylike2scalar( arrays[ 1 ] );
alt = ndarraylike2scalar( arrays[ 2 ] );
alpha = ndarraylike2scalar( arrays[ 3 ] );
mu = ndarraylike2scalar( arrays[ 4 ] );
sigma = ndarraylike2scalar( arrays[ 5 ] );
strided( numelDimension( x, 0 ), alt, alpha, mu, sigma, getData( x ), getStride( x, 0 ), getOffset( x ), y ); // eslint-disable-line max-len
return arrays[ 1 ];
}
// EXPORTS //
module.exports = ztest;
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