859 lines
21 KiB
Markdown
859 lines
21 KiB
Markdown
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## Vector Functionality
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### sum()
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**sum( array )**
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Returns the sum of the `array` vector.
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jStat.sum([1,2,3]) === 6
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**fn.sum( [bool][, callback] )**
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Returns the sum of a vector or matrix columns.
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jStat( 1, 5, 5 ).sum() === 15
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jStat([[1,2],[3,4]]).sum() === [ 4, 6 ]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).sum(function( result ) {
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// result === 15
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});
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If pass boolean true as first argument, then return sum of entire matrix.
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jStat([[1,2],[3,4]]).sum( true ) === 10
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And the two can be combined.
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jStat([[1,2],[3,4]]).sum(true, function( result ) {
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// result === 10
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});
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### sumsqrd()
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**sumsqrd( array )**
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Returns the sum squared of the `array` vector.
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jStat.sumsqrd([1,2,3]) === 14
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**fn.sumsqrd( [bool][, callback] )**
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Returns the sum squared of a vector or matrix columns.
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jStat( 1, 5, 5 ).sumsqrd() === 55
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jStat([[1,2],[3,4]]).sumsqrd() === [ 10, 20 ]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).sumsqrd(function( result ) {
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// result === 55
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});
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If pass boolean true as first argument, then return sum squared of entire matrix.
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jStat([[1,2],[3,4]]).sumsqrd( true ) === 650
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And the two can be combined.
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jStat([[1,2],[3,4]]).sumsqrd(true,function( result ) {
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// result === 650
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});
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### sumsqerr()
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**sumsqerr( array )**
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Returns the sum of squared errors of prediction of the `array` vector.
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jStat.sumsqerr([1,2,3]) === 2
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**fn.sumsqerr( [bool][, callback] )**
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Returns the sum of squared errors of prediction of a vector or matrix columns.
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jStat( 1, 5, 5 ).sumsqerr() === 10
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jStat([[1,2],[3,4]]).sumsqerr() === [ 2, 2 ]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).sumsqerr(function( result ) {
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// result === 55
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});
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If pass boolean true as first argument, then return sum of squared errors of entire matrix.
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jStat([[1,2],[3,4]]).sumsqerr( true ) === 0
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And the two can be combined.
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jStat([[1,2],[3,4]]).sumsqerr(true,function( result ) {
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// result === 0
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});
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### sumrow()
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**sumrow( array )**
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Returns the sum of the `array` vector in row-based order.
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jStat.sumrow([1,2,3]) === 6
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**fn.sumrow( [bool][, callback] )**
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Returns the sum of a vector or matrix rows.
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jStat( 1, 5, 5 ).sumrow() === 15
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jStat([[1,2],[3,4]]).sumrow() === [ 3, 7 ]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).sumrow(function( result ) {
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// result === 15
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});
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If pass boolean true as first argument, then return sum of entire matrix.
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jStat([[1,2],[3,4]]).sumrow( true ) === 10
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And the two can be combined.
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jStat([[1,2],[3,4]]).sumrow(true,function( result ) {
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// result === 10
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});
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### product()
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**product( array )**
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Returns the product of the `array` vector.
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jStat.product([1,2,3]) === 6
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**fn.product( [bool][, callback] )**
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Returns the product of a vector or matrix columns.
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jStat( 1, 5, 5 ).product() === 120
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jStat([[1,2],[3,4]]).product() === [ 3, 8 ]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).product(function( result ) {
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// result === 120
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});
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If pass boolean true as first argument, then return sumsqerr of entire matrix.
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jStat([[1,2],[3,4]]).product( true ) === 24
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And the two can be combined.
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jStat([[1,2],[3,4]]).product(true,function( result ) {
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// result === 24
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});
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### min()
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**min( array )**
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Returns the minimum value of the `array` vector.
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jStat.min([1,2,3]) === 1
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**fn.min( [bool][, callback] )**
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Returns the minimum value of a vector or matrix columns.
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jStat( 1, 5, 5 ).min() === 1
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jStat([[1,2],[3,4]]).min() === [ 1, 2 ]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).min(function( result ) {
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// result === 1
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});
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If pass boolean true as first argument, then return minimum of entire matrix.
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jStat([[1,2],[3,4]]).min( true ) === 1
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And the two can be combined.
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jStat([[1,2],[3,4]]).min(true,function( result ) {
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// result === 1
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});
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### max()
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**max( array )**
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Returns the maximum value of the `array` vector.
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jStat.max([1,2,3]) === 3
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**fn.max( [bool][, callback] )**
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Returns the maximum value of a vector or matrix columns.
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jStat( 1, 5, 5 ).max() === 5
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jStat([[1,2],[3,4]]).max() === [ 3, 4 ]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).max(function( result ) {
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// result === 5
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});
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If pass boolean true as first argument, then return maximum of entire matrix.
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jStat([[1,2],[3,4]]).max( true ) === 4
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And the two can be combined.
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jStat([[1,2],[3,4]]).max(true,function( result ) {
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// result === 4
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});
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### mean()
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**mean( array )**
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Returns the mean of the `array` vector.
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jStat.mean([1,2,3]) === 2
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**fn.max( [bool,][callback] )**
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Returns the max of a vector or matrix columns.
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jStat( 1, 5, 5 ).mean() === 3
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jStat([[1,2],[3,4]]).mean() === [ 2, 3 ]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).mean(function( result ) {
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// result === 3
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});
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If pass boolean true as first argument, then return mean of entire matrix.
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jStat([[1,2],[3,4]]).mean( true ) === 2.5
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And the two can be combined.
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jStat([[1,2],[3,4]]).mean(true,function( result ) {
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// result === 2.5
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});
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### meansqerr()
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**meansqerr( array )**
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Returns the mean squared error of the `array` vector.
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jStat.meansqerr([1,2,3]) === 0.66666...
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**fn.meansqerr( [bool][, callback] )**
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Returns the mean squared error of a vector or matrix columns.
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jStat( 1, 5, 5 ).meansqerr() === 2
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jStat([[1,2],[3,4]]).meansqerr() === [ 1, 1 ]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).meansqerr(function( result ) {
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// result === 2
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});
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If pass boolean true as first argument, then return mean squared error of entire matrix.
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jStat([[1,2],[3,4]]).meansqerr( true ) === 0
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And the two can be combined.
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jStat([[1,2],[3,4]]).meansqerr(true,function( result ) {
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// result === 0
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});
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### geomean()
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**geomean( array )**
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Returns the geometric mean of the `array` vector.
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jStat.geomean([4,1,1/32]) === 0.5
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**fn.geomean( [bool][, callback] )**
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Returns the geometric mean of a vector or matrix columns.
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jStat([4,1,1\32]).geomean() === 0.5
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jStat([[1,2],[3,4]]).geomean() === [ 1.732..., 2.828... ]
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If callback is passed then will pass result as first argument.
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jStat([4,1,1\32]).geomean(function( result ) {
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// result === 0.5
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});
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If pass boolean true as first argument, then return geometric mean of entire matrix.
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jStat([[1,2],[3,4]]).geomean( true ) === 2.213...
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And the two can be combined.
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jStat([[1,2],[3,4]]).geomean(true,function( result ) {
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// result === 2.213...
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});
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### median()
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**median( array )**
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Returns the median of the `array` vector.
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jStat.median([1,2,3]) === 2
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**fn.median( [bool][, callback] )**
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Returns the median of a vector or matrix columns.
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jStat( 1, 5, 5 ).median() === 3
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jStat([[1,2],[3,4]]).median() === [ 2, 3 ]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).median(function( result ) {
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// result === 3
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});
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If pass boolean true as first argument, then return median of entire matrix.
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jStat([[1,2],[3,4]]).median( true ) === 2.5
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And the two can be combined.
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jStat([[1,2],[3,4]]).median(true,function( result ) {
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// result === 2.5
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});
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### cumsum()
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**cumsum( array )**
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Returns an array of partial sums in the sequence.
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jStat.cumsum([1,2,3]) === [1,3,6]
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**fn.cumsum( [bool][, callback] )**
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Returns an array of partial sums for a vector or matrix columns.
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jStat( 1, 5, 5 ).cumsum() === [1,3,6,10,15]
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jStat([[1,2],[3,4]]).cumsum() === [[1,4],[2,6]]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).cumsum(function( result ) {
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// result === [1,3,6,10,15]
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});
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If pass boolean true as first argument, then return cumulative sums of the matrix.
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jStat([[1,2],[3,4]]).cumsum( true ) === [[1,3],[3,7]]
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And the two can be combined.
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jStat([[1,2],[3,4]]).cumsum(true,function( result ) {
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// result === ...
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});
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### cumprod()
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**cumprod( array )**
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Returns an array of partial products in the sequence.
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jStat.cumprod([2,3,4]) === [2,6,24]
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**fn.cumprod( [bool][, callback] )**
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Returns an array of partial products for a vector or matrix columns.
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jStat( 1, 5, 5 ).cumprod() === [1,2,6,24,120]
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jStat([[1,2],[3,4]]).cumprod() === [[1,3],[2,8]]
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If callback is passed then will pass result as first argument.
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jStat( 1, 5, 5 ).cumprod(function( result ) {
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// result === [1,2,6,24,120]
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});
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If pass boolean true as first argument, then return cumulative products of the matrix.
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jStat([[1,2],[3,4]]).cumprod( true ) === [[1,2],[3,12]]
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And the two can be combined.
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jStat([[1,2],[3,4]]).cumprod(true,function( result ) {
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// result === ...
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});
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### diff()
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**diff( array )**
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Returns an array of the successive differences of the array.
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||
|
|
jStat.diff([1,2,2,3]) === [1,0,1]
|
||
|
|
|
||
|
|
**fn.diff( [bool][, callback] )**
|
||
|
|
|
||
|
|
Returns an array of successive differences for a vector or matrix columns.
|
||
|
|
|
||
|
|
jStat([1,2,2,3]).diff() === [1,0,1]
|
||
|
|
jStat([[1,2],[3,4],[1,4]]).diff() === [[2,-2],[2,0]]
|
||
|
|
|
||
|
|
If callback is passed then will pass result as first argument.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,4],[1,4]]).diff(function( result ) {
|
||
|
|
// result === [[2,-2],[2,0]]
|
||
|
|
});
|
||
|
|
|
||
|
|
If pass boolean true as first argument, then return successive difference for the whole matrix.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,4],[1,4]]).diff(true) === [0,2]
|
||
|
|
|
||
|
|
And the two can be combined.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,4],[1,4]]).diff(true,function( result ) {
|
||
|
|
// result === [0,2]
|
||
|
|
});
|
||
|
|
|
||
|
|
### rank()
|
||
|
|
|
||
|
|
**rank( array )**
|
||
|
|
|
||
|
|
Returns an array of the ranks of the array.
|
||
|
|
|
||
|
|
jStat.rank([1, 2, 2, 3]) === [1, 2.5, 2.5, 4]
|
||
|
|
|
||
|
|
**fn.rank( [bool][, callback] )**
|
||
|
|
|
||
|
|
Returns an array of ranks for a vector or matrix columns.
|
||
|
|
|
||
|
|
jStat([1, 2, 2, 3]).rank() === [1, 2.5, 2.5, 4]
|
||
|
|
jStat([[1, 2], [3, 4], [1, 4]]).rank() === [[1.5, 3, 1.5], [1, 2.5, 2.5]]
|
||
|
|
|
||
|
|
If callback is passed then will pass result as first argument.
|
||
|
|
|
||
|
|
jStat([[1, 2], [3, 4], [1, 4]]).rank(function( result ) {
|
||
|
|
// result === [[1.5, 3, 1.5], [1, 2.5, 2.5]]
|
||
|
|
});
|
||
|
|
|
||
|
|
If pass boolean true as first argument, then return rank for the whole matrix.
|
||
|
|
|
||
|
|
jStat([[1, 2], [3, 4], [1, 4]]).rank(true) === [2, 5, 2, 5, 2, 5]
|
||
|
|
|
||
|
|
And the two can be combined.
|
||
|
|
|
||
|
|
jStat([[1, 2], [3, 4], [1, 4]]).rank(true, function( result ) {
|
||
|
|
// result === [2, 5, 2, 5, 2, 5]
|
||
|
|
});
|
||
|
|
|
||
|
|
### mode()
|
||
|
|
|
||
|
|
**mode( array )**
|
||
|
|
|
||
|
|
Returns the mode of the `array` vector.
|
||
|
|
If there are multiple modes then `mode()` will return all of them.
|
||
|
|
|
||
|
|
jStat.mode([1,2,2,3]) === 2
|
||
|
|
jStat.mode([1,2,3]) === [1,2,3]
|
||
|
|
|
||
|
|
**fn.mode( [bool][, callback] )**
|
||
|
|
|
||
|
|
Returns the mode for a vector or matrix columns.
|
||
|
|
|
||
|
|
jStat([1,2,2,3]).mode() === 2
|
||
|
|
jStat([[1,2],[3,4],[1,4]]).mode() === [1,4]
|
||
|
|
|
||
|
|
If callback is passed then will pass result as first argument.
|
||
|
|
|
||
|
|
jStat( 1, 5, 5 ).mode(function( result ) {
|
||
|
|
// result === false
|
||
|
|
});
|
||
|
|
|
||
|
|
If pass boolean true as first argument, then the matrix will be treated as one
|
||
|
|
dimensional.
|
||
|
|
|
||
|
|
jStat([[5,4],[5, 2], [5,2]]).mode( true ) === 5
|
||
|
|
|
||
|
|
### range()
|
||
|
|
|
||
|
|
**range( array )**
|
||
|
|
|
||
|
|
Returns the range of the `array` vector.
|
||
|
|
|
||
|
|
jStat.range([1,2,3]) === 2
|
||
|
|
|
||
|
|
**fn.range( [bool][, callback] )**
|
||
|
|
|
||
|
|
Returns the range for a vector or matrix columns.
|
||
|
|
|
||
|
|
jStat([1,2,3]).range() === 2
|
||
|
|
jStat([[1,2],[3,4]]).range() === [2,2]
|
||
|
|
|
||
|
|
If callback is passed then will pass result as first argument.
|
||
|
|
|
||
|
|
jStat( 1, 5, 5 ).range(function( result ) {
|
||
|
|
// result === 4
|
||
|
|
});
|
||
|
|
|
||
|
|
If pass boolean true as first argument, then return range of the matrix.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).range( true ) === true
|
||
|
|
|
||
|
|
And the two can be combined.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).range(true,function( result ) {
|
||
|
|
// result === 1
|
||
|
|
});
|
||
|
|
|
||
|
|
### variance()
|
||
|
|
|
||
|
|
**variance( array[, flag] )**
|
||
|
|
|
||
|
|
Returns the variance of the `array` vector.
|
||
|
|
By default, the population variance is calculated.
|
||
|
|
Passing `true` to `flag` indicates to compute the sample variance instead.
|
||
|
|
|
||
|
|
jStat.variance([1,2,3,4]) === 1.25
|
||
|
|
jStat.variance([1,2,3,4],true) === 1.66666...
|
||
|
|
|
||
|
|
**fn.variance( [bool][, callback] )**
|
||
|
|
|
||
|
|
Returns the variance for a vector or matrix columns.
|
||
|
|
|
||
|
|
**Note:** Cannot pass flag to indicate between population or sample for matrices.
|
||
|
|
There is a feature request for this on [Issue #51](https://github.com/jstat/jstat/issues/51).
|
||
|
|
|
||
|
|
jStat([1,2,3,4]).variance() === 1.25
|
||
|
|
jStat([[1,2],[3,4]]).variance() === [1,1]
|
||
|
|
|
||
|
|
If callback is passed then will pass result as first argument.
|
||
|
|
|
||
|
|
jStat( 1, 5, 5 ).variance(function( result ) {
|
||
|
|
// result === 2
|
||
|
|
});
|
||
|
|
|
||
|
|
If pass boolean true as first argument, then return variance of the matrix.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).variance( true ) === 0.140625
|
||
|
|
|
||
|
|
And the two can be combined.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).variance(true,function( result ) {
|
||
|
|
// result === 0.140625
|
||
|
|
});
|
||
|
|
|
||
|
|
### pooledvariance()
|
||
|
|
|
||
|
|
**pooledvariance( arrays )**
|
||
|
|
|
||
|
|
Returns the pooled (sample) variance of an array of vectors.
|
||
|
|
Assumes the population variance of the vectors are the same.
|
||
|
|
|
||
|
|
jStat.pooledvariance([[1,2],[3,4]]) === 0.5
|
||
|
|
|
||
|
|
### deviation()
|
||
|
|
|
||
|
|
**deviation( array )**
|
||
|
|
|
||
|
|
Returns the deviation of the `array` vector.
|
||
|
|
|
||
|
|
jStat.deviation([1,2,3,4]) === [-1.5, -0.5, 0.5, 1.5]
|
||
|
|
|
||
|
|
**fn.deviation( [bool][, callback] )**
|
||
|
|
|
||
|
|
Returns the deviation for a vector or matrix columns.
|
||
|
|
|
||
|
|
jStat([1,2,3,4]).deviation() === [-1.5, -0.5, 0.5, 1.5]
|
||
|
|
jStat([[1,2],[3,4]]).deviation() === [[-1,1],[-1,1]]
|
||
|
|
|
||
|
|
If callback is passed then will pass result as first argument.
|
||
|
|
|
||
|
|
jStat( 1, 4, 4 ).deviation(function( result ) {
|
||
|
|
// result === [-1.5, -0.5, 0.5, 1.5]
|
||
|
|
});
|
||
|
|
|
||
|
|
If pass boolean true as first argument, then return variance of the matrix.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).deviation( true ) === [-0.5, 0.5, -1, 1]
|
||
|
|
|
||
|
|
And the two can be combined.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).deviation(true,function( result ) {
|
||
|
|
// result === [-0.5, 0.5, -1, 1]
|
||
|
|
});
|
||
|
|
|
||
|
|
### stdev()
|
||
|
|
|
||
|
|
**stdev( array[, flag] )**
|
||
|
|
|
||
|
|
Returns the standard deviation of the `array` vector.
|
||
|
|
By default, the population standard deviation is returned.
|
||
|
|
Passing `true` to `flag` returns the sample standard deviation.
|
||
|
|
|
||
|
|
The 'sample' standard deviation is also called the 'corrected standard deviation', and is an unbiased estimator of the population standard deviation.
|
||
|
|
The population standard deviation is also the 'uncorrected standard deviation', and is a biased but minimum-mean-squared-error estimator.
|
||
|
|
|
||
|
|
jStat.stdev([1,2,3,4]) === 1.118...
|
||
|
|
jStat.stdev([1,2,3,4],true) === 1.290...
|
||
|
|
|
||
|
|
**fn.stdev( [bool][, callback] )**
|
||
|
|
|
||
|
|
Returns the standard deviation for a vector or matrix columns.
|
||
|
|
|
||
|
|
**Note:** Cannot pass `flag` to indicate between population or sample for matrices.
|
||
|
|
There is a feature request for this on [Issue #51](https://github.com/jstat/jstat/issues/51).
|
||
|
|
|
||
|
|
jStat([1,2,3,4]).stdev() === 1.118...
|
||
|
|
jStat([1,2,3,4]).stdev(true) === 1.290...
|
||
|
|
jStat([[1,2],[3,4]]).stdev() === [1,1]
|
||
|
|
|
||
|
|
If callback is passed then will pass result as first argument.
|
||
|
|
|
||
|
|
jStat( 1, 4, 4 ).stdev(function( result ) {
|
||
|
|
// result === 1.118...
|
||
|
|
});
|
||
|
|
jStat( 1, 4, 4 ).stdev(true,function( result ) {
|
||
|
|
// result === 1.290...
|
||
|
|
});
|
||
|
|
|
||
|
|
If pass boolean true as first argument, then return variance of the matrix.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).stdev( true ) === 0.25
|
||
|
|
|
||
|
|
And the two can be combined.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).stdev(true,function( result ) {
|
||
|
|
// result === 0.25
|
||
|
|
});
|
||
|
|
|
||
|
|
### pooledstdev()
|
||
|
|
|
||
|
|
**pooledstdev( arrays )**
|
||
|
|
|
||
|
|
Returns the pooled (sample) standard deviation of an array of vectors.
|
||
|
|
Assumes the population standard deviation of the vectors are the same.
|
||
|
|
|
||
|
|
jStat.pooledstdev([[1,2],[3,4]]) === 0.707...
|
||
|
|
|
||
|
|
### meandev()
|
||
|
|
|
||
|
|
**meandev( array )**
|
||
|
|
|
||
|
|
Returns the mean absolute deviation of the `array` vector.
|
||
|
|
|
||
|
|
jStat.meandev([1,2,3,4]) === 1
|
||
|
|
|
||
|
|
**fn.meandev( [bool][, callback] )**
|
||
|
|
|
||
|
|
Returns the mean absolute deviation for a vector or matrix columns.
|
||
|
|
|
||
|
|
jStat([1,2,3,4]).meandev() === 1
|
||
|
|
jStat([[1,2],[3,4]]).meandev() === [1,1]
|
||
|
|
|
||
|
|
If callback is passed then will pass result as first argument.
|
||
|
|
|
||
|
|
jStat( 1, 4, 4 ).meandev(function( result ) {
|
||
|
|
// result === 1
|
||
|
|
});
|
||
|
|
|
||
|
|
If pass boolean true as first argument, then return mean absolute deviation of the matrix.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).meandev( true ) === 0.25
|
||
|
|
|
||
|
|
And the two can be combined.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).meandev(true,function( result ) {
|
||
|
|
// result === 0.25
|
||
|
|
});
|
||
|
|
|
||
|
|
### meddev()
|
||
|
|
|
||
|
|
**meddev( array )**
|
||
|
|
|
||
|
|
Returns the median absolute deviation of the `array` vector.
|
||
|
|
|
||
|
|
jStat.meddev([1,2,3,4]) === 1
|
||
|
|
|
||
|
|
**fn.meddev( [bool][, callback] )**
|
||
|
|
|
||
|
|
Returns the median absolute deviation for a vector or matrix columns.
|
||
|
|
|
||
|
|
jStat([1,2,3,4]).meddev() === 1
|
||
|
|
jStat([[1,2],[3,4]]).meddev() === [1,1]
|
||
|
|
|
||
|
|
If callback is passed then will pass result as first argument.
|
||
|
|
|
||
|
|
jStat( 1, 4, 4 ).meddev(function( result ) {
|
||
|
|
// result === 1
|
||
|
|
});
|
||
|
|
|
||
|
|
If pass boolean true as first argument, then return median absolute deviation of the matrix.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).meddev( true ) === 0.25
|
||
|
|
|
||
|
|
And the two can be combined.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).meddev(true,function( result ) {
|
||
|
|
// result === 0.25
|
||
|
|
});
|
||
|
|
|
||
|
|
### skewness()
|
||
|
|
|
||
|
|
**skewness( array )**
|
||
|
|
|
||
|
|
Returns the skewness of the `array` vector (third standardized moment).
|
||
|
|
|
||
|
|
jStat.skewness([1,2,2,3,5]) === 0.75003...
|
||
|
|
|
||
|
|
### kurtosis()
|
||
|
|
|
||
|
|
**kurtosis( array )**
|
||
|
|
|
||
|
|
Returns the excess kurtosis of the `array` vector (fourth standardized moment - 3).
|
||
|
|
|
||
|
|
jStat.kurtosis([1,2,3,4]) === -0.63610...
|
||
|
|
|
||
|
|
### coeffvar()
|
||
|
|
|
||
|
|
**coeffvar( array )**
|
||
|
|
|
||
|
|
Returns the coefficient of variation of the `array` vector.
|
||
|
|
|
||
|
|
jStat.coeffvar([1,2,3,4]) === 0.447...
|
||
|
|
|
||
|
|
**fn.coeffvar( [bool][, callback] )**
|
||
|
|
|
||
|
|
Returns the coefficient of variation for a vector or matrix columns.
|
||
|
|
|
||
|
|
jStat([1,2,3,4]).coeffvar() === 0.447...
|
||
|
|
jStat([[1,2],[3,4]]).coeffvar() === [0.5,0.333...]
|
||
|
|
|
||
|
|
If callback is passed then will pass result as first argument.
|
||
|
|
|
||
|
|
jStat( 1, 4, 4 ).coeffvar(function( result ) {
|
||
|
|
// result === 0.447...
|
||
|
|
});
|
||
|
|
|
||
|
|
If pass boolean true as first argument, then return coefficient of variation of the matrix.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).coeffvar( true ) === 0.142...
|
||
|
|
|
||
|
|
And the two can be combined.
|
||
|
|
|
||
|
|
jStat([[1,2],[3,5]]).coeffvar(true,function( result ) {
|
||
|
|
// result === 0.142...
|
||
|
|
});
|
||
|
|
|
||
|
|
### quartiles()
|
||
|
|
|
||
|
|
**quartiles( array )**
|
||
|
|
|
||
|
|
Returns the quartiles of the `array` vector.
|
||
|
|
|
||
|
|
jStat.quartiles( jStat.seq(1,100,100)) === [25,50,75]
|
||
|
|
|
||
|
|
**fn.quartiles( [callback] )**
|
||
|
|
|
||
|
|
Returns the quartiles for a vector or matrix columns.
|
||
|
|
|
||
|
|
jStat(1,100,100).quartiles() === [25,50,75]
|
||
|
|
jStat(1,100,100,function( x ) {
|
||
|
|
return [x,x];
|
||
|
|
}).quartiles() === [[25,50,75],[25,50,75]]
|
||
|
|
|
||
|
|
If callback is passed then will pass result as first argument.
|
||
|
|
|
||
|
|
jStat(1,100,100).quartiles(function( result ) {
|
||
|
|
// result === [25,50,75]
|
||
|
|
});
|
||
|
|
|
||
|
|
### quantiles()
|
||
|
|
|
||
|
|
**quantiles( dataArray, quantilesArray[, alphap[, betap]] )**
|
||
|
|
|
||
|
|
Like quartiles, but calculate and return arbitrary quantiles of the `dataArray` vector
|
||
|
|
or matrix (column-by-column).
|
||
|
|
|
||
|
|
jStat.quantiles([1, 2, 3, 4, 5, 6],
|
||
|
|
[0.25, 0.5, 0.75]) === [1.9375, 3.5, 5.0625]
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Optional parameters alphap and betap govern the quantile estimation method.
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For more details see the Wikipedia page on quantiles or scipy.stats.mstats.mquantiles
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|
documentation.
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### percentile()
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**percentile( dataArray, k, [exclusive] )**
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Returns the k-th percentile of values in the `dataArray` range, where k is in the range 0..1, exclusive.
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Passing true for the exclusive parameter excludes both endpoints of the range.
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||
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jStat.percentile([1, 2, 3, 4], 0.3) === 1.9;
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jStat.percentile([1, 2, 3, 4], 0.3, true) === 1.5;
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|
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### percentileOfScore()
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|
|
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|
|
**percentileOfScore( dataArray, score[, kind] )**
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|
|
The percentile rank of score in a given array. Returns the percentage
|
||
|
|
of all values in `dataArray` that are less than (if `kind == 'strict'`) or
|
||
|
|
less or equal than (if `kind == 'weak'`) score. Default is `'weak'`.
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||
|
|
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||
|
|
jStat.percentileOfScore([1, 2, 3, 4, 5, 6], 3), 0.5, 'weak') === 0.5;
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||
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|
|
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|
|
### histogram()
|
||
|
|
|
||
|
|
**histogram( dataArray[, numBins] )**
|
||
|
|
|
||
|
|
The histogram data defined as the number of `dataArray` elements found in
|
||
|
|
equally sized bins across the range of `dataArray`. Default number
|
||
|
|
of bins is 4.
|
||
|
|
|
||
|
|
jStat.histogram([100, 101, 102, 230, 304, 305, 400], 3) === [3, 1, 3];
|
||
|
|
|
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|
|
### covariance()
|
||
|
|
|
||
|
|
**covariance( array1, array2 )**
|
||
|
|
|
||
|
|
Returns the covariance of the `array1` and `array2` vectors.
|
||
|
|
|
||
|
|
var seq = jStat.seq( 0, 10, 11 );
|
||
|
|
jStat.covariance( seq, seq ) === 11;
|
||
|
|
|
||
|
|
### corrcoeff()
|
||
|
|
|
||
|
|
**corrcoeff( array1, array2 )**
|
||
|
|
|
||
|
|
Returns the population correlation coefficient of the `array1` and `array2` vectors (Pearson's Rho).
|
||
|
|
|
||
|
|
var seq = jStat.seq( 0, 10, 11 );
|
||
|
|
jStat.corrcoeff( seq, seq ) === 1;
|
||
|
|
|
||
|
|
|
||
|
|
**spearmancoeff( array1, array2 )**
|
||
|
|
|
||
|
|
Returns the rank correlation coefficient of the `array1` and `array2` vectors (Spearman's Rho).
|
||
|
|
|
||
|
|
jStat.spearmancoeff([1, 2, 3, 4], [5, 6, 9, 7]) == 0.8;
|
||
|
|
jStat.spearmancoeff([1, 2, 2, 4], [5, 2, 5, 7]) == 0.5;
|