82 lines
2.5 KiB
Plaintext
82 lines
2.5 KiB
Plaintext
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{{alias}}( W[, options] )
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Returns an accumulator function which incrementally performs a moving
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Grubbs' test for detecting outliers.
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Grubbs' test assumes that data is normally distributed. Accordingly, one
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should first verify that the data can be reasonably approximated by a normal
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distribution before applying the Grubbs' test.
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The `W` parameter defines the number of values over which to perform Grubbs'
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test. The minimum window size is 3.
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If provided a value, the accumulator function returns updated test results.
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If not provided a value, the accumulator function returns the current test
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results.
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Until provided `W` values, the accumulator function returns `null`.
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The accumulator function returns an object having the following fields:
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- rejected: boolean indicating whether the null hypothesis should be
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rejected.
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- alpha: significance level.
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- criticalValue: critical value.
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- statistic: test statistic.
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- df: degrees of freedom.
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- mean: sample mean.
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- sd: corrected sample standard deviation.
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- min: minimum value.
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- max: maximum value.
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- alt: alternative hypothesis.
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- method: method name.
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- print: method for pretty-printing test output.
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Parameters
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----------
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W: integer
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Window size.
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options: Object (optional)
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Function options.
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options.alpha: number (optional)
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Significance level. Default: 0.05.
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options.alternative: string (optional)
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Alternative hypothesis. The option may be one of the following values:
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- 'two-sided': test whether the minimum or maximum value is an outlier.
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- 'min': test whether the minimum value is an outlier.
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- 'max': test whether the maximum value is an outlier.
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Default: 'two-sided'.
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Returns
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-------
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acc: Function
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Accumulator function.
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Examples
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--------
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> var acc = {{alias}}( 20 );
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> var res = acc()
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null
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> for ( var i = 0; i < 200; i++ ) {
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... res = acc( {{alias:@stdlib/random/base/normal}}( 10.0, 5.0 ) );
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... };
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> res.print()
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References
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----------
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- Grubbs, Frank E. 1950. "Sample Criteria for Testing Outlying
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Observations." _The Annals of Mathematical Statistics_ 21 (1). The Institute
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of Mathematical Statistics: 27–58. doi:10.1214/aoms/1177729885.
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- Grubbs, Frank E. 1969. "Procedures for Detecting Outlying Observations in
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Samples." _Technometrics_ 11 (1). Taylor & Francis: 1–21. doi:10.1080/
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00401706.1969.10490657.
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See Also
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--------
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