MATLAB Answers

Can you organize scatter plot points?

조회 수: 2(최근 30일)
Dan Griffiths
Dan Griffiths 2021년 5월 5일
답변: Steven Lord 2021년 5월 7일
Hi,
I would like to graph my data with the mean +/- SEM and display the data points. When I plot this, my data points are overlapping and difficult to distinguish. Rather than a random distribution/jitter, I would like my data points for same/similiar values to form nonoverlaping rows of points. Is this poissible in Matlab? Any help is greatly appreciated!
Thanks,
Dan
Example code:
subplot 133
% Plot horizontal line at 50%
yline(0.5,'k--','LineWidth',ylineLW)
hold on
% Plot data points
scatter(y1(:,1),data(:,1),'black','filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitter','randn')
scatter(y1(:,2),data(:,2),'red','filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitter','randn')
scatter(y1(:,3),data(:,3),'black','filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitter','randn')
scatter(y1(:,4),data(:,4),'red','filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitter','randn')
% Plot errorbar (SEM)
errorbar(y2(1),mData(1),SEM(1),'k_','LineWidth',errorbarLW)
errorbar(y2(2),mData(2),SEM(2),'r_','LineWidth',errorbarLW)
errorbar(y2(3),mData(3),SEM(3),'k_','LineWidth',errorbarLW)
errorbar(y2(4),mData(4),SEM(4),'r_','LineWidth',errorbarLW)
% Plot mean values
plot(y2(1),mData(1),'k_','LineWidth',plotLW,'MarkerSize',plotMS)
plot(y2(2),mData(2),'r_','LineWidth',plotLW,'MarkerSize',plotMS)
plot(y2(3),mData(3),'k_','LineWidth',plotLW,'MarkerSize',plotMS)
plot(y2(4),mData(4),'r_','LineWidth',plotLW,'MarkerSize',plotMS)
hold off
PS I attached one of my plots to help visualize the problem. Please let me know if you have any questions... thanks again!
  댓글 수: 12
Dan Griffiths
Dan Griffiths 2021년 5월 7일
We would like to plot the mean +/- error with overlay of the data points. Can you do this with histograms? I have made histograms, but never to do anything like this... Can you share an example? Thanks!

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채택된 답변

Matt J
Matt J 2021년 5월 7일
편집: Matt J 2021년 5월 7일
I think it may make more sense for you to move to a proper violin plot. With this 3rd party file, for example
you can generate plots like the one below. The violin envelope clearly delineates the boundaries of the estimated distribution for each level y, which I think is ultimately what you want.
load Data.mat
hv=violinplot(data,{'Sham','TBI'});
ylabel 'Discrimination Ratio'
for i=1:2
hv(i).ViolinAlpha=0.1;
hv(i).ScatterPlot.MarkerFaceAlpha=0.7;
end
hv(1).ViolinColor='k';
hv(2).ViolinColor='r';
If you wish, the jittered data points can be overlaid as well:
[hv.ShowData]=deal(true); %remove if you don't want data points included
  댓글 수: 2
dpb
dpb 2021년 5월 7일
y=sort(data(:,1));
delta=0.075;
dx=zeros(size(y));
dx(1:2)=delta*[-1 1].';
dx(6:end-3)=delta*repmat(-1:1,1,4);
hSc=scatter(x+dx,y,'black','filled');
xlim([0 5])
produces
I didn't work on the differences algorithm much, but look at something like
find(diff(y<=thresh))
where thresh is about 0.02. If you want more separation, increase dx.
I can count 20 individual points in the above, although a few are still touching with above choices.
ADDENDUM: Remember the length(diff(y)) is one less than length(y) when locating indices to modify.

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추가 답변(2개)

Matt J
Matt J 2021년 5월 7일
편집: Matt J 2021년 5월 7일
This may be more along the lines of what you were originally looking for
load Data;
w=max( max(data,[],1) - min(data,[],1) ); %Maximum swarm width
xoffset=w; %separation between swarms
P=size(data,2);
for n=1:P
y=data(:,n);
bins=linspace(min(y),max(y),15);
[counts,~,G]=histcounts(y,bins);
sumc=sum(counts);
x=nan(size(y));
for i=1:numel(counts)
J=(G==i);
N=counts(i);
if N<2
x(J)=0;
else
x(J)=(w*N/sumc)*linspace(-0.5,+0.5,N);
end
end
x=x+n*xoffset;
h(i)=scatter(x,y,'filled');
axis equal
hold on
end
hold off
xticks((1:P)*xoffset)
xlim([xoffset/2,P*w+xoffset/2])
xticklabels({'Sham','TBI'})

Steven Lord
Steven Lord 2021년 5월 7일
When I look at your plot it looks to me an awful lot like the boxplot thumbnail in the Plots tab of the Toolstrip. You may want to investigate if that can display the information in a less cluttered approach than a simple scatter plot.

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