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