| Description |
The zip-file contains the following files for visualizing distributions:
- distributionPlot.m: main function that allows creating violin plots
- histogram.m: generate histograms with 'ideal' bin width given the number of data points and the spread (Freedman-Diaconis rule). Note that for integer-valued data, each integer gets its own bin.
- plotSpread.m: plot point clouds with no overlap. Works well for small number of data points, i.e. when there are less than ~20 values per bin.
In addition, the zip file contains four helper functions: countEntries, colorCode2rgb, isEven, myErrorbar
DistributionPlot allows visualizing multiple distributions side by side. It is useful for skewed unimodal data and indispensable for multimodal data. DistributionPlot is especially useful for showing the time evolution of a distribution.
Some of the examples from the help:
r = rand(1000,1);
rn = randn(1000,1)*0.38+0.5;
rn2 = [randn(500,1)*0.1+0.27;randn(500,1)*0.1+0.73];
rn2=min(rn2,1);rn2=max(rn2,0);
figure
ah(1)=subplot(2,4,1:2);
boxplot([r,rn,rn2])
ah(2)=subplot(2,4,3:4);
distributionPlot([r,rn,rn2],'histOpt',2); % histOpt=2 works better for uniform distributions than the default
set(ah,'ylim',[-1 2])
%--additional options
data = [randn(100,1);randn(50,1)+4;randn(25,1)+8];
subplot(2,4,5)
distributionPlot(data); % defaults
subplot(2,4,6)
distributionPlot(data,'colormap',copper,'showMM',5,'variableWidth',false) % show density via custom colormap only, show mean/std,
subplot(2,4,7:8)
distributionPlot({data(1:5:end),repmat(data,2,1)},'addSpread',true,'showMM',false,'histOpt',2) %auto-binwidth depends on # of datapoints; for small n, plotting the data is useful
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