marginal plot

marginalPlot: Visualization of grouped data with main plot and marginal plots Creates (scatter, contour, regression, etc.) main plot with upper (X-axis) and right (Y-axis) marginal distributions (histogram, boxplot, violin, rug, etc.). A total of 99 combinations are currently available.

Demo 1 : Basic usage

% Generate three Gaussian-distributed datasets
Data1 = mvnrnd([ 2, 3], [1, 0;0, 2], 300);
Data2 = mvnrnd([ 6, 7], [1, 0;0, 2], 300);
Data3 = mvnrnd([14, 9], [1, 0;0, 1], 300);
DataSet  = {Data1, Data2, Data3};
% Create marginal plot object and draw
MP = marginalPlot(DataSet, 'MainType',6, 'UpperType',5, 'RightType',10);
MP.draw();

% =========================================================================
% MainType (Main Plot) Options
% =========================================================================
%  No. | Type         | Description
% -----|--------------|----------------------------------------------------
%   1  | 'scatter'    | Scatter plot with filled markers
%   2  | 'contour'    | Contour plot based on kernel density estimation
%   3  | 'pred'       | Linear regression with prediction interval (polyfit)
%   4  | 'convhull'   | Scatter plot with convex hull outline
%   5  | 'cover'      | Scatter plot with buffered/expanded convex hull (rounded)
%   6  | 'ellipse'    | Scatter plot with confidence ellipse (99%)
%   7  | 'centroid'   | Star plot: points connected to group centroid
%   8  | 'errorbar'   | Cross error bar (mean ± std) for each group
%   9  | 'conf'       | Linear regression with confidence interval (fitlm)
% ---------------------------------------------------------------------------
% =========================================================================
% UpperType / RightType (Marginal Plot) Options
% =========================================================================
%  No. | Type           | Description
% -----|----------------|--------------------------------------------------
%   1  | 'hist'         | Standard histogram
%   2  | 'kd-area'      | Kernel density estimation area (filled)
%   3  | 'kd-line'      | Kernel density estimation line only
%   4  | 'kd-both'      | Kernel density area + line
%   5  | 'kd-hist'      | Histogram overlayed with kernel density line
%   6  | 'box'          | Box plot (median, quartiles, outliers)
%   7  | 'violin'       | Full violin plot (symmetric KDE)
%   8  | 'rug'          | Rug plot (vertical/horizontal line scatter)
%   9  | 'joyplot'      | Joyplot / Ridgeline plot (stacked KDE)
%  10  | 'half-violin'  | Half violin plot (right side only, compact)
%  11  | 'raincloud'    | Raincloud plot (half-violin + scatter)
% -------------------------------------------------------------------------

Demo 2 : Change colors and labels

% Generate three Gaussian-distributed datasets
Data1 = mvnrnd([ 2, 3], [1, 0;0, 2], 300);
Data2 = mvnrnd([ 6, 7], [1, 0;0, 2], 300);
Data3 = mvnrnd([14, 9], [1, 0;0, 1], 300);
DataSet  = {Data1, Data2, Data3};
% Create marginal plot object and draw
MP = marginalPlot(DataSet, 'MainType',6, 'UpperType',5, 'RightType',10);
MP.ClassName = {'AAAAA','BBBBB','CCCCC'};
MP.CData = [122,117,119; 255,163, 25; 135,146, 73; 126, 15,  4;  30, 93,134]./255;
MP.draw();
MP.axM.XLabel.String = 'Thank U very much for your five-star review !!!';
MP.axM.YLabel.String = 'Rate me please.';
MP.axU.YLabel.String = 'Marginal plot';
MP.axR.XLabel.String = 'Made by slandarer';