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How to create variability charts?

Asked by Eric Sampson on 1 Feb 2013
Latest activity Commented on by Eric Sampson on 18 Oct 2013

I'm trying to find a way to recreate JMP-style variability charts using MATLAB.

I've tried looking through the Stats Toolbox and the File Exchange, and can't find anything that would do the trick. Anyone have an idea?

Thanks!

3 Comments

Matt Tearle on 1 Feb 2013

How important is the formatting detail? A boxplot will do the job, but look quite different. Which details of the appearance are important to you?

Eric Sampson on 1 Feb 2013

Matt, the critical part is how it understands the hierarchical nature of the groups (in the example, note how oil amt is a subset of batch size, which is a subset of popcorn type), and also how it includes that understanding in the layout of the X axis labeling. The end users are accustomed to this chart layout, and expect to see the same look.

Shashank Prasanna on 1 Feb 2013

If you know how the 'understanding is included' in that chart, you can use that information and create a boxplot. There is nothing this specialized that is offered in the statistics toolbox. You could put in a ticket with the mathworks as a suggested enhancement along with your usecase.

Eric Sampson

2 Answers

Answer by Matt Tearle on 1 Feb 2013
Edited by Matt Tearle on 22 Mar 2013
Accepted answer

EDIT: file added to MATLAB File Exchange. Share and enjoy!

Based on your comment above, boxplot with nominal grouping variables will do it:

x = randn(400,1);
y1 = nominal(round(rand(400,1)),{'little','lots'});
y2 = nominal(round(rand(400,1)),{'large','small'});
y3 = nominal(round(rand(400,1)),{'gourmet','plain'});
boxplot(x,[y1,y2,y3])

Hopefully this is basically how your data is already arranged. x contains all 400 observations of the response variable. y1, y2, and y3 are nominal arrays that record each observation's status for the three categories.

The boxplot labeling doesn't emphasize the hierarchy, but the results are correct.

EDIT TO ADD Oops, I got the grouping variables backward. Anyway, this is getting close to what you posted:

boxplot(x,[y3,y2,y1],...
  'plotstyle','compact','labelorientation','horizontal',...
  'factorseparator',[1,2])

The only problem is that the vertical arrangement of the group labels is backwards, for showing the hierarchy. This can be hacked, though, if you need:

h = findobj(get(gca,'children'),'type','text');
tl = get(h,'position');
tl = cat(1,tl{:});
tl(:,2) = flipud(tl(:,2));
for k = 1:length(h)
   set(h(k),'position',tl(k,:))
end

EDIT TO ADD (2): Not pretty, but here's a function that does a reasonable job of approximating the graphic:

function variabilityplot(x,y)
n = size(y,2);
numgrps = zeros(1,n);
for k = 1:n
    numgrps(k) = numel(unique(y(:,k)));
end
numgrps = cumprod(numgrps);
N = numgrps(n);
y = fliplr(y);
boxplot(x,y,...
    'plotstyle','compact','labelorientation','horizontal',...
    'factorseparator',1:n);
hbxplt = get(gca,'children');
hall = get(hbxplt,'children');
halltype = get(hall,'type');
hsepln = hall(end-n+1:end);
htxt = hall(strcmpi('text',halltype));
set(htxt,'units','data')
txtpos = get(htxt,'position');
txtpos = cat(1,txtpos{:});
txtpos(:,2) = flipud(txtpos(:,2));
x = reshape(txtpos(:,1),N,n);
for k = 2:n
    m = numgrps(k-1);
    for j = 1:N
        ii = floor((j-1)/m);
        i1 = 1 + m*ii;
        i2 = m*(1+ii);
        x(j,k) = mean(x(i1:i2,1));
    end
end
txtpos(:,1) = x(:);
for k = 1:length(htxt)
    set(htxt(k),'position',txtpos(k,:))
end
tlcol = 0.5*[1,1,1];
txtpos = get(htxt,'extent');
txtpos = cat(1,txtpos{:});
xl = xlim;
yl = ylim;
y1 = min(yl);
y2 = min(txtpos(:,2));
y = linspace(y1,y2,n+1);
for k = 2:(n+1)
    line(xl,[y(k),y(k)],'parent',gca,'clipping','off','color',tlcol)
end
line(xl(1)*[1,1],[y1,y2],'parent',gca,'clipping','off','color',tlcol)
line(xl(2)*[1,1],[y1,y2],'parent',gca,'clipping','off','color',tlcol)
for j = 1:n
    newy = get(hsepln(j),'YData');
    newy(newy==yl(2)) = y(j+1);
    line(get(hsepln(j),'XData'),newy,'parent',gca,'clipping','off','color',tlcol)
end
delete(hsepln(1))

Trying it out:

x = randn(400,1);
y1 = nominal(randi(2,400,1),{'little','lots'});
y2 = nominal(randi(3,400,1),{'large','medium','small'});
y3 = nominal(randi(2,400,1),{'gourmet','plain','aardvark','potato'},[1,2,3,4]);
y = [y1,y2,y3];
variabilityplot(x,y)

If you think it's useful, I'll clean it up a bit and put it on the File Exchange soon.

8 Comments

Eric Sampson on 11 Feb 2013

Matt, that second code you posted works great! I think it's definitely worth a FileEx posting. I'd take you out for a beer too, if you were a little closer :) The only thing that it's missing in order to be perfect is being robust to changing the size of the figure, if you're up for a challenge!

Matt Tearle on 22 Mar 2013

@Eric: sorry it took a while, but if you're still interested, I've now added it to the FEx (link is in my answer above). The figure resizing issue is tricky -- in the end I took the easy(ish) way out and just turned that aspect off entirely.

Eric Sampson on 18 Oct 2013

@Matt thanks very much! Somehow I missed seeing your last comment, so my reply is even later :) Best, Eric

Matt Tearle
Answer by Shashank Prasanna on 1 Feb 2013

You can certainly use boxplots: http://www.mathworks.com/help/stats/boxplot.html

But I am not certain there is something that generates a plot that looks exactly like that. You may have to generate a boxplot and add all the labels below them after that.

0 Comments

Shashank Prasanna

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