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

The function scatterplot with the format parameter format=agglomerated of the subpackage stats[statplots] organizes data clusters into classes.

This type of plot is often used by cartographers, or 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 plotted instead as one box.

When n or more points occur within a cube with side length l those points are replaced by the tightest fitting box possible.  The tightest fitting box in one-dimension will always be a line.  In higher dimensions, rectangles or boxes may be plotted.

When $n^2$ or more points occur within the same cube described in the last paragraph, the box is emphasized by being plotted with thicker lines.

If n or l are zero, or unspecified, default values will be used.  The default value of n is the cube root of the number of points, divided by the number of dimensions of the data (that is, number of statistical lists passed as parameters).  The default value for l is one-tenth the range of the x-coordinate data.

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.

$\mathrm{with}\left(\mathrm{stats}\right)\:$

$\mathrm{with}\left(\mathrm{stats}\left[\mathrm{statplots}\right]\right)\:$

$\mathrm{data1}≔\left[\mathrm{random}\left[\mathrm{normald}\right]\left(30\right)\,\mathrm{random}\left[\mathrm{normald}\left[3,1\right]\right]\left(20\right)\right]\:$

$\mathrm{data2}≔\left[\mathrm{random}\left[\mathrm{normald}\right]\left(30\right)\,\mathrm{random}\left[\mathrm{normald}\left[3,1\right]\right]\left(20\right)\right]\:$

$\mathrm{scatterplot}\left(\mathrm{data1}\,\mathrm{data2}\,\mathrm{format}=\mathrm{agglomerated}\left[1,0.5\right]\right)$

$\mathrm{data3}≔\left[12.00\,\mathrm{Weight}\left(10\,3\right)\,8..9.5\,9.67\,11.11\,10.34\right]\:$

$\mathrm{scatterplot}\left(\mathrm{data3}\,\mathrm{format}=\mathrm{agglomerated}\right)$