statplots(deprecated)/agglomerated - Help

stats[statplots, scatterplot]

Agglomerated Plot

 Calling Sequence stats[statplots, scatterplot](data, format=agglomerated[n, l], ..) statplots[scatterplot](data, format=agglomerated[n, l], ..) scatterplot(data, format=agglomerated[n, l], ..)

Parameters

 data - n - number of points within the range determined by l l - maximum length of one side of an agglomerated box plotoptions -

Description

 • 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.

Examples

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

 > $\mathrm{with}\left(\mathrm{stats}\right):$
 > $\mathrm{with}\left({\mathrm{stats}}_{\mathrm{statplots}}\right):$
 > $\mathrm{data1}≔\left[\mathrm{random}[\mathrm{normald}]\left(30\right),\mathrm{random}[\mathrm{normald}[3,1]]\left(20\right)\right]:$
 > $\mathrm{data2}≔\left[\mathrm{random}[\mathrm{normald}]\left(30\right),\mathrm{random}[\mathrm{normald}[3,1]]\left(20\right)\right]:$
 > $\mathrm{scatterplot}\left(\mathrm{data1},\mathrm{data2},\mathrm{format}={\mathrm{agglomerated}}_{1,0.5}\right)$

1-D case

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