Analyze data with the Statistics software R: Difference between revisions

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<html>
<html>
<script language="JavaScript">
<script language="JavaScript">
         brd = JXG.JSXGraph.initBoard('jxgbox', {originX: 20, originY: 300, unitX: 60, unitY: 5});
         brd = JXG.JSXGraph.initBoard('jxgbox', {originX: 20, originY: 300, unitX: 60, unitY: 5,axis:true});
         brd.suspendUpdate();
         brd.suspendUpdate();
        b1axisx = brd.createElement('axis', [[0,0], [1,0]], {ticksDelta:5, strokeColor:'#000000'});
        b1axisy = brd.createElement('axis', [[0,0], [0,1]], {ticksDelta:5, strokeColor:'#000000'});
        b1axisx.ticksDelta = 5;
        b1axisy.ticksDelta = 5;
        brd.fullUpdate = true;
        brd.update();
        brd.fullUpdate = false;
       
         var graph1,graph2,graph3,graph4,graph5,graph6,graph7,graph8,graph9;
         var graph1,graph2,graph3,graph4,graph5,graph6,graph7,graph8,graph9;


Revision as of 11:39, 10 March 2009

Normal Location and Scale

This litte application sends the y-coordinates of the points which are normal distributed (pseudo-)random numbers to the server.
There, location and scale of the sample are estimated using the Statistics software R.
The return values are plotted and displayed.

The computed estimates are:

  • mean, standard deviation: red (non-robust!)
  • median and MAD: black (most-robust!)
  • radius-minimax estimator: green (optimally robust; cf. Rieder et al. (2008))

By changing the y-position of the four movable points you should recognize the instability (non-robustness) of mean and standard deviation in contrast to the robust estimates; e.g., move one of the four movable points to the top of the plot.

Online results:

Statistics:<br>

The underlying source code

The underlying JavaScript and PHP code

The R script can be downloaded here.

References

  • The Costs of not Knowing the Radius, Helmut Rieder, Matthias Kohl and Peter Ruckdeschel, Statistical Methods and Application 2008 Feb; 17(1): p.13-40; cf. also [1] for an extended version.
  • Robust Asymptotic Statistics, Helmut Rieder, Springer, 1994.
  • Numerical Contributions to the Asymptotic Theory of Robustness, Matthias Kohl, PhD-Thesis, University of Bayreuth, 2005; cf. also [2].

External links

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