 statplots(deprecated)/excised - Maple Help

stats[statplots, scatterplot]

Excise Plot Calling Sequence stats[statplots, scatterplot](data, format=excised[p], ..) statplots[scatterplot](data, format=excised[p], ..) scatterplot(data, format=excised[p], ..) Parameters

 data - p - fraction of data points to remove from the plot plotoptions - Description

 • Important: The stats package has been deprecated. Use the superseding package Statistics instead.
 • The function scatterplot with the format parameter format=excised of the subpackage stats[statplots] removes the fraction of least dense data and plots a scatterplot of the remaining data.
 • This type of plot is often used when there is a large number of data points involved.  The idea is that it is not necessary to have the detail of each individual point in a plot.  Closely grouped points are supposedly more representative, so all other points are excised.
 • When p is a positive number between zero and one, p times the number of points are excised from the plot.  In other words only the $\left(1-p\right)\mathrm{numpoints}$ most densely clustered points are plotted in the scatter plot.
 • When p is a negative number between zero and -1, $p\mathrm{numpoints}$ of the most densely clustered points are excised from the plot.  In this case $\left(1-p\right)\mathrm{numpoints}$ of the least densely clustered points are plotted in the scatter plot.
 • The default, when p is not specified is zero.  This plot is identical to a scatter plot with no format parameter.
 • Relative density of points is calculated approximately by finding the distances between all points using the distance metric $\mathrm{exp}\left(-|\mathrm{p1}-\mathrm{p2}|\right)$.
 • Class data is converted to classmarks before generating the plot.  Weighted data is accounted for.  Missing data is ignored.
 • The command with(stats[statplots]) allows the use of the abbreviated form of this command. Examples

Important: The stats package has been deprecated. Use the superseding package Statistics instead.

 > $\mathrm{with}\left(\mathrm{stats}\right):$
 > $\mathrm{with}\left(\mathrm{stats}\left[\mathrm{statplots}\right]\right):$
 > $\mathrm{data1}≔\left[\mathrm{random}\left[\mathrm{normald}\right]\left(30\right),\mathrm{random}\left[\mathrm{normald}\left[3,1\right]\right]\left(20\right)\right]:$
 > $\mathrm{data2}≔\left[\mathrm{random}\left[\mathrm{normald}\right]\left(30\right),\mathrm{random}\left[\mathrm{normald}\left[3,1\right]\right]\left(20\right)\right]:$
 > $\mathrm{scatterplot}\left(\mathrm{data1},\mathrm{data2},\mathrm{format}=\mathrm{excised}\left[0.5\right]\right)$ > $\mathrm{data3}≔\left[12.00,\mathrm{Weight}\left(10,3\right),8..9.5,9.67,11.11,10.34\right]:$
 > $\mathrm{scatterplot}\left(\mathrm{data3},\mathrm{format}=\mathrm{excised}\right)$ 