# dnanmeanors > Calculate the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation.
The [arithmetic mean][arithmetic-mean] is defined as
Equation for the arithmetic mean.
## Usage ```javascript var dnanmeanors = require( '@stdlib/stats/base/dnanmeanors' ); ``` #### dnanmeanors( N, x, stride ) Computes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array `x`, ignoring `NaN` values and using ordinary recursive summation. ```javascript var Float64Array = require( '@stdlib/array/float64' ); var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] ); var N = x.length; var v = dnanmeanors( N, x, 1 ); // returns ~0.3333 ``` The function has the following parameters: - **N**: number of indexed elements. - **x**: input [`Float64Array`][@stdlib/array/float64]. - **stride**: index increment for `x`. The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] of every other element in `x`, ```javascript var Float64Array = require( '@stdlib/array/float64' ); var floor = require( '@stdlib/math/base/special/floor' ); var x = new Float64Array( [ 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 = dnanmeanors( N, x, 2 ); // returns 1.25 ``` Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views. ```javascript var Float64Array = require( '@stdlib/array/float64' ); var floor = require( '@stdlib/math/base/special/floor' ); var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] ); var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element var N = floor( x0.length / 2 ); var v = dnanmeanors( N, x1, 2 ); // returns 1.25 ``` #### dnanmeanors.ndarray( N, x, stride, offset ) Computes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation and alternative indexing semantics. ```javascript var Float64Array = require( '@stdlib/array/float64' ); var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] ); var N = x.length; var v = dnanmeanors.ndarray( N, x, 1, 0 ); // returns ~0.33333 ``` The function has the following additional parameters: - **offset**: starting index for `x`. While [`typed array`][mdn-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 [arithmetic mean][arithmetic-mean] for every other value in `x` starting from the second value ```javascript var Float64Array = require( '@stdlib/array/float64' ); var floor = require( '@stdlib/math/base/special/floor' ); var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] ); var N = floor( x.length / 2 ); var v = dnanmeanors.ndarray( N, x, 2, 1 ); // returns 1.25 ```
## Notes - If `N <= 0`, both functions return `NaN`. - If every indexed element is `NaN`, both functions return `NaN`. - Ordinary recursive summation (i.e., a "simple" sum) is performant, but can incur significant numerical error. If performance is paramount and error tolerated, using ordinary recursive summation to compute an arithmetic mean is acceptable; in all other cases, exercise due caution.
## Examples ```javascript var randu = require( '@stdlib/random/base/randu' ); var round = require( '@stdlib/math/base/special/round' ); var Float64Array = require( '@stdlib/array/float64' ); var dnanmeanors = require( '@stdlib/stats/base/dnanmeanors' ); var x; var i; x = new Float64Array( 10 ); for ( i = 0; i < x.length; i++ ) { if ( randu() < 0.2 ) { x[ i ] = NaN; } else { x[ i ] = round( (randu()*100.0) - 50.0 ); } } console.log( x ); var v = dnanmeanors( x.length, x, 1 ); console.log( v ); ```