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You may also be interested in the article I posted a few years ago about another undocumented scatterplot behavior. The rest are common to all Handle Graphic objects. Of these properties, the following are unique to scatter plots: Dirty, Initialized, Jitter, JitterAmount, RefreshMode. This list can be retrieved using my getundoc utility: MarkDirtyProp = Lappend(markDirtyProp,hProp) Other hidden properties of scatterplotsįor the record, here is a list of the other hidden properties of scatterplot. HProp.Description = 'Maximum amount of jitter'

HProp = schema.prop(hClass, 'JitterAmount', 'double') % Change this, 'double' => 'MATLAB Array' MarkDirtyProp = Lappend(markDirtyProp,hProp) HProp.Description = 'Enable/disable jittering' HProp = schema.prop(hClass, 'Jitter', 'on/off') Description = 'Maximum amount of jitter'

prop (hClass, 'JitterAmount', 'double' ) % Change this, 'double' => 'MATLAB Array' MarkDirtyProp = Lappend (markDirtyProp,hProp ) Description = 'Enable/disable jittering' Jittering the data introduces minor inaccuracies to the data, which some may find anathema, but it does solve the visualization problem: The solution that is generally used in such cases is to either use some other plot type to convey the density information (e.g., histograms, CDF or quantile plots), or to keep using scatter plots but jitter the data just a tiny bit that would enable users to visualize the density more clearly. When we wish to visually convey density distributions of real values this could lead to erroneous assumptions about the data. Standard scatter plot - cannot see distribution densityīoth data points look exactly the same and no amount of zooming-in will discover that there are 100 data points in the center data point compared to only a single data point in the top-right and lower-left. % Display in a scatterplot scatter (dataX, dataY )