time-to-botec

README.md (10255B)

---

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 # incrmgrubbs
 
 > Moving [Grubbs' test][grubbs-test] for outliers.
 
 <section class="intro">
 
 [Grubbs' test][grubbs-test] (also known as the **maximum normalized residual test** or **extreme studentized deviate test**) is a statistical test used to detect outliers in a univariate dataset assumed to come from a normally distributed population. [Grubbs' test][grubbs-test] is defined for the hypothesis:
 
 -   **H_0**: the dataset does **not** contain outliers.
 -   **H_1**: the dataset contains **exactly** one outlier.
 
 For a window of size `W`, the [Grubbs' test][grubbs-test] statistic for a two-sided alternative hypothesis is defined as
 
 <!-- <equation class="equation" label="eq:grubbs_test_statistic" align="center" raw="G = \frac{\max_{i=0,\ldots,W-1} |Y_i - \bar{Y}|}{s}" alt="Grubbs' test statistic."> -->
 
 <div class="equation" align="center" data-raw-text="G = \frac{\max_{i=0,\ldots,W-1} |Y_i - \bar{Y}|}{s}" data-equation="eq:grubbs_test_statistic">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@d157db87026794d6fab579039be09a8916cad4e0/lib/node_modules/@stdlib/stats/incr/mgrubbs/docs/img/equation_grubbs_test_statistic.svg" alt="Grubbs' test statistic.">
     <br>
 </div>
 
 <!-- </equation> -->
 
 where `s` is the sample standard deviation. The [Grubbs test][grubbs-test] statistic is thus the largest absolute deviation from the sample mean in units of the sample standard deviation.
 
 The [Grubbs' test][grubbs-test] statistic for the alternative hypothesis that the minimum value is an outlier is defined as
 
 <!-- <equation class="equation" label="eq:grubbs_test_statistic_min" align="center" raw="G = \frac{\bar{Y} - Y_{\textrm{min}}}{s}" alt="Grubbs' test statistic for testing whether the minimum value is an outlier."> -->
 
 <div class="equation" align="center" data-raw-text="G = \frac{\bar{Y} - Y_{\textrm{min}}}{s}" data-equation="eq:grubbs_test_statistic_min">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@d157db87026794d6fab579039be09a8916cad4e0/lib/node_modules/@stdlib/stats/incr/mgrubbs/docs/img/equation_grubbs_test_statistic_min.svg" alt="Grubbs' test statistic for testing whether the minimum value is an outlier.">
     <br>
 </div>
 
 <!-- </equation> -->
 
 The [Grubbs' test][grubbs-test] statistic for the alternative hypothesis that the maximum value is an outlier is defined as
 
 <!-- <equation class="equation" label="eq:grubbs_test_statistic_max" align="center" raw="G = \frac{Y_{\textrm{max}} - \bar{Y}}{s}" alt="Grubbs' test statistic for testing whether the maximum value is an outlier."> -->
 
 <div class="equation" align="center" data-raw-text="G = \frac{Y_{\textrm{max}} - \bar{Y}}{s}" data-equation="eq:grubbs_test_statistic_max">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@d157db87026794d6fab579039be09a8916cad4e0/lib/node_modules/@stdlib/stats/incr/mgrubbs/docs/img/equation_grubbs_test_statistic_max.svg" alt="Grubbs' test statistic for testing whether the maximum value is an outlier.">
     <br>
 </div>
 
 <!-- </equation> -->
 
 For a two-sided test, the hypothesis that a dataset does **not** contain an outlier is rejected at significance level α if
 
 <!-- <equation class="equation" label="eq:grubbs_test_two_sided" align="center" raw="G > \frac{W-1}{\sqrt{W}} \sqrt{\frac{t^2_{\alpha/(2W),W-2}}{W - 2 + t^2_{\alpha/(2W),W-2}}}" alt="Two-sided Grubbs' test."> -->
 
 <div class="equation" align="center" data-raw-text="G > \frac{W-1}{\sqrt{W}} \sqrt{\frac{t^2_{\alpha/(2W),W-2}}{W - 2 + t^2_{\alpha/(2W),W-2}}}" data-equation="eq:grubbs_test_two_sided">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@d157db87026794d6fab579039be09a8916cad4e0/lib/node_modules/@stdlib/stats/incr/mgrubbs/docs/img/equation_grubbs_test_two_sided.svg" alt="Two-sided Grubbs' test.">
     <br>
 </div>
 
 <!-- </equation> -->
 
 where `t` denotes the upper critical value of the _t_-distribution with `W-2` degrees of freedom and a significance level of `α/(2W)`.
 
 For a one-sided test, the hypothesis that a dataset does **not** contain an outlier is rejected at significance level α if
 
 <!-- <equation class="equation" label="eq:grubbs_test_one_sided" align="center" raw="G > \frac{W-1}{\sqrt{W}} \sqrt{\frac{t^2_{\alpha/W,W-2}}{W - 2 + t^2_{\alpha/W,W-2}}}" alt="One-sided Grubbs' test."> -->
 
 <div class="equation" align="center" data-raw-text="G > \frac{W-1}{\sqrt{W}} \sqrt{\frac{t^2_{\alpha/W,W-2}}{W - 2 + t^2_{\alpha/W,W-2}}}" data-equation="eq:grubbs_test_one_sided">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@d157db87026794d6fab579039be09a8916cad4e0/lib/node_modules/@stdlib/stats/incr/mgrubbs/docs/img/equation_grubbs_test_one_sided.svg" alt="One-sided Grubbs' test.">
     <br>
 </div>
 
 <!-- </equation> -->
 
 where `t` denotes the upper critical value of the _t_-distribution with `W-2` degrees of freedom and a significance level of `α/W`.
 
 </section>
 
 <!-- /.intro -->
 
 <section class="usage">
 
 ## Usage
 
 ```javascript
 var incrmgrubbs = require( '@stdlib/stats/incr/mgrubbs' );
 ```
 
 #### incrmgrubbs( window\[, options] )
 
 Returns an accumulator `function` which incrementally performs [Grubbs' test][grubbs-test] for outliers. The `window` parameter defines the number of values over which to perform [Grubbs' test][grubbs-test].
 
 ```javascript
 var accumulator = incrmgrubbs( 20 );
 ```
 
 The function accepts the following `options`:
 
 -   **alpha**: significance level. Default: `0.05`.
 
 -   **alternative**: 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'`.
 
 #### accumulator( \[x] )
 
 If provided an input value `x`, the accumulator function returns updated test results. If not provided an input value `x`, the accumulator function returns the current test results.
 
 ```javascript
 var rnorm = require( '@stdlib/random/base/normal' );
 
 var accumulator = incrmgrubbs( 3 );
 
 var results = accumulator( rnorm( 10.0, 5.0 ) );
 // returns null
 
 results = accumulator( rnorm( 10.0, 5.0 ) );
 // returns null
 
 results = accumulator( rnorm( 10.0, 5.0 ) );
 // returns <Object>
 
 results = accumulator();
 // returns <Object>
 ```
 
 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.
 
 The `print` method accepts the following options:
 
 -   **digits**: number of digits after the decimal point. Default: `4`.
 -   **decision**: `boolean` indicating whether to print the test decision. Default: `true`.
 
 </section>
 
 <!-- /.usage -->
 
 <section class="notes">
 
 ## Notes
 
 -   [Grubbs' test][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][grubbs-test].
 -   The minimum `window` size is `3`. In general, the larger the `window`, the more robust outlier detection will be. However, larger windows entail increased memory consumption.
 -   Until `window` values have been provided, the accumulator returns `null`.
 -   Input values are **not** type checked. If provided `NaN` or a value which, when used in computations, results in `NaN`, the accumulated test statistic is `NaN` for **at least** `W-1` future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly **before** passing the value to the accumulator function.
 
 </section>
 
 <!-- /.notes -->
 
 <section class="examples">
 
 ## Examples
 
 <!-- eslint no-undef: "error" -->
 
 ```javascript
 var sensorData = require( '@stdlib/datasets/suthaharan-single-hop-sensor-network' );
 var incrmgrubbs = require( '@stdlib/stats/incr/mgrubbs' );
 
 var data;
 var opts;
 var acc;
 var N;
 var r;
 var i;
 
 // Get a test dataset:
 data = sensorData();
 N = 0;
 for ( i = 0; i < data.length; i++ ) {
     if ( data[ i ].mote_id === 1 ) {
         N += 1;
         data[ i ] = data[ i ].temperature;
     }
 }
 data.length = N;
 
 // Create a new accumulator which analyzes the last 5 minutes of data:
 opts = {
     'alternative': 'two-sided'
 };
 acc = incrmgrubbs( 60, opts );
 
 // Update the accumulator:
 for ( i = 0; i < data.length; i++ ) {
     r = acc( data[ i ] );
     if ( r && r.rejected ) {
         console.log( 'Index: %d', i );
         console.log( '' );
         console.log( r.print() );
     }
 }
 ```
 
 </section>
 
 <!-- /.examples -->
 
 <section class="references">
 
 * * *
 
 ## 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:1950a].
 -   Grubbs, Frank E. 1969. "Procedures for Detecting Outlying Observations in Samples." _Technometrics_ 11 (1). Taylor & Francis: 1–21. doi:[10.1080/00401706.1969.10490657][@grubbs:1969a].    
 
 </section>
 
 <!-- /.references -->
 
 <section class="links">
 
 [grubbs-test]: https://en.wikipedia.org/wiki/Grubbs%27_test_for_outliers
 
 [@grubbs:1950a]: https://doi.org/10.1214/aoms/1177729885
 
 [@grubbs:1969a]: https://doi.org/10.1080/00401706.1969.10490657
 
 </section>
 
 <!-- /.links -->