Difference between revisions of "Analyze data with the Statistics software R"
From JSXGraph Wiki
Jump to navigationJump to search| Line 33: | Line 33: | ||
y[6+i] = 10+z2*3; | y[6+i] = 10+z2*3; | ||
x[6+i] = 6+i; | x[6+i] = 6+i; | ||
| − | p[i] = brd.createElement('point', [x[i],y[i]],{name:' ',fixed:true, | + | p[i] = brd.createElement('point', [x[i],y[i]],{name:' ',fixed:true,size:2,face:'[]'}); |
| − | p[6+i] = brd.createElement('point', [x[6+i],y[6+i]],{name:' ',fixed:true, | + | p[6+i] = brd.createElement('point', [x[6+i],y[6+i]],{name:' ',fixed:true,size:2,face:'[]'}); |
}else{ | }else{ | ||
y[i] = 10+z1*3; | y[i] = 10+z1*3; | ||
| − | p[i] = brd.createElement('point', [x[i],y[i]],{name:' ',fixed:true, | + | p[i] = brd.createElement('point', [x[i],y[i]],{name:' ',fixed:true,size:2,face:'[]'}); |
} | } | ||
} | } | ||
| Line 61: | Line 61: | ||
brd.removeObject(p[9]); | brd.removeObject(p[9]); | ||
| − | p[0] = brd.createElement('glider', [x[0],y[0],l[0]],{name:' ', | + | p[0] = brd.createElement('glider', [x[0],y[0],l[0]],{name:' ',size:4,face:'o'}); |
| − | p[10] = brd.createElement('glider', [x[10],y[10],l[1]],{name:' ', | + | p[10] = brd.createElement('glider', [x[10],y[10],l[1]],{name:' ',size:4,face:'o'}); |
| − | p[8] = brd.createElement('glider', [x[8],y[8],l[2]],{name:' ', | + | p[8] = brd.createElement('glider', [x[8],y[8],l[2]],{name:' ',size:4,face:'o'}); |
| − | p[9] = brd.createElement('glider', [x[9],y[9],l[3]],{name:' ', | + | p[9] = brd.createElement('glider', [x[9],y[9],l[3]],{name:' ',size:4,face:'o'}); |
brd.unsuspendUpdate(); | brd.unsuspendUpdate(); | ||
| Line 127: | Line 127: | ||
}}); | }}); | ||
} | } | ||
| + | brd.addHook(doIt, 'mouseup'); | ||
</jsxgraph> | </jsxgraph> | ||
Revision as of 11:16, 7 June 2011
Contents
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].
