How to get a normal box plot graph we get in origin software which consist of box plot with data points scattered and normal distribution curve in MATLAB

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Deepan Jayakumar
Deepan Jayakumar 2024-3-27
评论： Voss 2024-3-28
I am trying to plot a boxplot in matlab which should contain boxplot with data points in the right with normal distribution curve in matlab. Like the one above

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Voss
Voss 2024-3-27
Ran in:
Maybe something like this. This fits distributions to the data, but if you have the distributions already you can plot them the same way.
% random normal data
data = randn(100,3);
% box plot and distribution colors
colors = [0 0.6 0; 0.8 0.8 0; 0.8 0 0];
% box plot width
w = 0.3;
[N,m] = size(data);
% make the box plot
h = boxplot(data,'Widths',w);
% shift box plots to the left by w/2
xd = arrayfun(@(obj)get(obj,'XData')-w/2,h,'UniformOutput',false);
set(h,{'XData'},xd(:))
% set box plot colors
for ii = 1:m
set(h(:,ii),'Color',colors(ii,:),'MarkerEdgeColor',colors(ii,:))
end
hold on
% fit and plot the distributions
[xmin,xmax] = bounds(data(:));
x = linspace(xmin,xmax);
for ii = 1:m
plot(ii+w/4+w/2*rand(N,1),data(:,ii),'.','Color',colors(ii,:))
pd = fitdist(data(:,ii),'Normal');
plot(pdf(pd,x)+ii,x,'Color',colors(ii,:))
end
% set axes xticks and xticklabels
set(gca(),'XTick',1:m,'XTickLabels',string(char('A'+(0:m-1).')))

更多回答（2 个）

Aquatris
Aquatris 2024-3-27
移动：Image Analyst 2024-3-27
I dont think there is a builtin plot like this. You can create your own functions for it or modify the community provided ones such as this.
Malay Agarwal
Malay Agarwal 2024-3-28
Ran in:
Hi Deepan,
I understand that you want to create a boxplot with the normal distribution and the datapoints for each category overlayed to the right of each box.
Please try the following code:
% Step 1: Generate Sample Data
rng(10); % For reproducibility
dataA = normrnd(20, 5, [100, 1]);
dataB = normrnd(30, 10, [100, 1]);
dataC = normrnd(40, 15, [100, 1]);
categories = [repmat({'A'}, 100, 1); repmat({'B'}, 100, 1); repmat({'C'}, 100, 1)];
values = [dataA; dataB; dataC];
% Define colors for each category
colors = {'magenta', 'green', 'blue'}; % Magenta for A, Green for B, Blue for C
% Step 2: Create Boxplot
boxplot(values, categories, 'Whisker', Inf);
hold on;
% Step 3: Plot Normal Distributions next to each boxplot
uniqueCats = unique(categories, 'stable');
for i = 1:length(uniqueCats)
category = uniqueCats{i};
subset = values(strcmp(categories, category));
% Creating a range of values for plotting the normal distribution
x = linspace(min(subset), max(subset), 100);
% Calculating the normal distribution with the same mean and std as the data
pd = fitdist(subset, 'Normal');
y = pdf(pd, x);
% Scaling the y-values to match the boxplot size
y = y / max(y) * 0.15; % 0.15 is the scaling factor, adjust as needed
% Plotting the normal distribution with specified color
plot(i + y, x, 'Color', colors{i});
% Step 4: Overlay scatter plot for the actual data points
% Adjust position of each data point to align with the distribution curve
% We scatter the points with a slight random offset horizontally to avoid overlap
rng(10); % For consistent scatter positions
scatter_x = normrnd(i, 0.03, size(subset)) + max(y); % Adjust scatter_x for alignment
% Scatter with specified color and transparency
scatter(scatter_x, subset, [], colors{i}, 'filled', 'MarkerFaceAlpha', 0.3);
end
title('Boxplot with Normal Distribution and Data Points');
hold off;
The code:
  • Uses the “boxplot” function to create the initial boxplot.
  • Uses “hold on” to ensure that all subsequent plots are on the same figure.
  • Loops over each category and fits a normal distribution to the category’s data using the “fitdist” function.
  • Creates a PDF from the normal distribution using the “pdf” function and plots it.
  • Creates a scatter plot for the category’s data using the “scatter” function and plots it.
Please refer to the following resources for more information:
Hope this helps!

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