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See:
Description
Interface Summary | |
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BoxPlotDataProvider |
Class Summary | |
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Box | |
BoxPlotDrawingPane | Paints the Box es, the dots from
the DotInfoArray
(since it derives from
ScatterPlotterDrawingPane )
and the labels for the boxes and outliers. |
BoxPlotNodeFactory | |
BoxPlotNodeModel | The input data is sorted for each numeric column and the necessary parameters are determined: minimum, lower whisker (in case of outliers it is the first non-outlier), lower quartile, median, upper quartile, upper whisker and maximum. |
BoxPlotNodeView | |
BoxPlotter | The BoxPlotter calculates, based on the statistical
parameters determined by the
BoxPlotNodeModel , the
Box es to
draw in the updateSize method. |
BoxPlotterProperties | Tab to select whether to normalize the drawing or not. |
ConditionalBoxPlotter |
Contains all classes that realize a box plot. A box plot displays some robust statistcal parameters for numerical attributes and identifies extreme outliers. A box plot displays the robust statistical parameters: minimum, lower quartile, median, upper quartile and maximum. These parameters called robust, since they are not sensitive to extreme outliers.
A box plot for one numerical attriubte is constructed in the following way: The box itself goes from the lower quartile (Q1) to the upper quartile (Q3). The median is drawn as a horizontal bar inside the box. The distance between Q1 and Q3 is called the interquartile range (IQR).
Above and below the box are the so-called whiskers. They are drawn at the minimum and the maximum value as horizontal bars and are connected with the box with a dotted line. The whiskers never exceed 1.5 * IQR. This means if there are some data points which exceed either Q1 - (1.5 * IQR) or Q3 + (1.5 * IQR) than the whiskers are drawn at exactly these ranges and the data points are drawn seperatly as outliers.
For the outliers the distinction between mild and extreme outliers is made.
As mild outliers are those datapoints p considered for which holds:
p < Q1 - (1.5 * IQR) and p > Q1 - (3 * IQR) or
p > Q3 + (1.5 * IQR) and p < Q3 + (3 * IQR).
In other words mild outliers are those data points which lay between 1.5 * IRQ
and 3 * IRQ. Extreme outliers are those datapoints p for which holds:
p < Q1 - (3 * IQR) or p > Q3 + (3 * IQR).
Thus, three times the box width (IQR) marks the boundary between
"mild" and "extreme" outliers. Mild outliers are painted as dots while extreme
outliers are displayed as crosses. In order to identify the outliers they can
be selected and hilited. This provides a quick overview over extreme
charateristics of a dataset.
The visual model (i.e. the boxes) are created by the
BoxPlotNodeModel
which implements the
BoxPlotDataProvider
, an interface
to pass the necessary parameters to the
BoxPlotter
.
The BoxPlotter
maps the values to the
screen coordinates and passes the mapped
Box
es to the
BoxPlotDrawingPane
.
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