Difference between revisions of "Analyze data with the Statistics software R"

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Line 88: Line 88:
 
                         var sd = a[1]*1.0;
 
                         var sd = a[1]*1.0;
 
                         var med = a[2]*1.0;
 
                         var med = a[2]*1.0;
                        var mad = a[3]*1.0;
 
 
                         var mad = a[3]*1.0;
 
                         var mad = a[3]*1.0;
 
                         var est1 = a[4]*1.0;
 
                         var est1 = a[4]*1.0;
 
                         var est2 = a[5]*1.0;
 
                         var est2 = a[5]*1.0;
  
console.log(t);
+
console.log(t, res, m, sd, med, mad, est1, est2);
 
                         if (!graph2) {  
 
                         if (!graph2) {  
 
                             graph2 = brd.createElement('curve', [[x[0],x[x.length-1]],[m,m]], {strokecolor:'red'});  
 
                             graph2 = brd.createElement('curve', [[x[0],x[x.length-1]],[m,m]], {strokecolor:'red'});  

Revision as of 12:33, 21 February 2013

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