178 lines
5.1 KiB
Matlab
178 lines
5.1 KiB
Matlab
function sout = myboxplot (data,notched,symbol,vertical,maxwhisker)
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% sout = myboxplot (data,notched,symbol,vertical,maxwhisker)
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% Written by Marco Ratto
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% Joint Research Centre, The European Commission,
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% marco.ratto@ec.europa.eu
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% Copyright (C) 2012 European Commission
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% Copyright (C) 2012 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% % % % endif
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if nargin < 5 | isempty(maxwhisker), maxwhisker = 1.5; end
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if nargin < 4 | isempty(vertical), vertical = 1; end
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if nargin < 3 | isempty(symbol), symbol = ['+','o']; end
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if nargin < 2 | isempty(notched), notched = 0; end
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if length(symbol)==1, symbol(2)=symbol(1); end
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if notched==1, notched=0.25; end
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a=1-notched;
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% ## figure out how many data sets we have
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if iscell(data),
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nc = length(data);
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else
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% if isvector(data), data = data(:); end
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nc = size(data,2);
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end
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% ## compute statistics
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% ## s will contain
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% ## 1,5 min and max
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% ## 2,3,4 1st, 2nd and 3rd quartile
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% ## 6,7 lower and upper confidence intervals for median
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s = zeros(7,nc);
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box = zeros(1,nc);
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whisker_x = ones(2,1)*[1:nc,1:nc];
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whisker_y = zeros(2,2*nc);
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outliers_x = [];
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outliers_y = [];
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outliers2_x = [];
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outliers2_y = [];
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for i=1:nc
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% ## Get the next data set from the array or cell array
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if iscell(data)
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col = data{i}(:);
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else
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col = data(:,i);
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end
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% ## Skip missing data
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% % % % % % % col(isnan(col) | isna (col)) = [];
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col(isnan(col)) = [];
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% ## Remember the data length
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nd = length(col);
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box(i) = nd;
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if (nd > 1)
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% ## min,max and quartiles
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% s(1:5,i) = statistics(col)(1:5);
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s(1,i)=min(col);
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s(5,i)=max(col);
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s(2,i)=myprctilecol(col,25);
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s(3,i)=myprctilecol(col,50);
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s(4,i)=myprctilecol(col,75);
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% ## confidence interval for the median
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est = 1.57*(s(4,i)-s(2,i))/sqrt(nd);
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s(6,i) = max([s(3,i)-est, s(2,i)]);
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s(7,i) = min([s(3,i)+est, s(4,i)]);
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% ## whiskers out to the last point within the desired inter-quartile range
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IQR = maxwhisker*(s(4,i)-s(2,i));
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whisker_y(:,i) = [min(col(col >= s(2,i)-IQR)); s(2,i)];
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whisker_y(:,nc+i) = [max(col(col <= s(4,i)+IQR)); s(4,i)];
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% ## outliers beyond 1 and 2 inter-quartile ranges
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outliers = col((col < s(2,i)-IQR & col >= s(2,i)-2*IQR) | (col > s(4,i)+IQR & col <= s(4,i)+2*IQR));
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outliers2 = col(col < s(2,i)-2*IQR | col > s(4,i)+2*IQR);
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outliers_x = [outliers_x; i*ones(size(outliers))];
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outliers_y = [outliers_y; outliers];
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outliers2_x = [outliers2_x; i*ones(size(outliers2))];
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outliers2_y = [outliers2_y; outliers2];
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elseif (nd == 1)
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% ## all statistics collapse to the value of the point
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s(:,i) = col;
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% ## single point data sets are plotted as outliers.
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outliers_x = [outliers_x; i];
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outliers_y = [outliers_y; col];
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else
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% ## no statistics if no points
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s(:,i) = NaN;
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end
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end
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% % % % if isempty(outliers2_y)
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% % % % outliers2_y=
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% ## Note which boxes don't have enough stats
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chop = find(box <= 1);
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% ## Draw a box around the quartiles, with width proportional to the number of
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% ## items in the box. Draw notches if desired.
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box = box*0.23/max(box);
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quartile_x = ones(11,1)*[1:nc] + [-a;-1;-1;1;1;a;1;1;-1;-1;-a]*box;
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quartile_y = s([3,7,4,4,7,3,6,2,2,6,3],:);
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% ## Draw a line through the median
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median_x = ones(2,1)*[1:nc] + [-a;+a]*box;
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% median_x=median(col);
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median_y = s([3,3],:);
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% ## Chop all boxes which don't have enough stats
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quartile_x(:,chop) = [];
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quartile_y(:,chop) = [];
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whisker_x(:,[chop,chop+nc]) = [];
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whisker_y(:,[chop,chop+nc]) = [];
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median_x(:,chop) = [];
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median_y(:,chop) = [];
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% % % %
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% ## Add caps to the remaining whiskers
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cap_x = whisker_x;
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cap_x(1,:) =cap_x(1,:)- 0.05;
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cap_x(2,:) =cap_x(2,:)+ 0.05;
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cap_y = whisker_y([1,1],:);
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% #quartile_x,quartile_y
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% #whisker_x,whisker_y
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% #median_x,median_y
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% #cap_x,cap_y
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%
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% ## Do the plot
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mm=min(min(data));
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MM=max(max(data));
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if vertical
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plot (quartile_x, quartile_y, 'b', ...
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whisker_x, whisker_y, 'b--', ...
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cap_x, cap_y, 'k', ...
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median_x, median_y, 'r', ...
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outliers_x, outliers_y, [symbol(1),'r'], ...
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outliers2_x, outliers2_y, [symbol(2),'r']);
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set(gca,'XTick',1:nc);
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set(gca, 'XLim', [0.5, nc+0.5]);
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set(gca, 'YLim', [mm-(MM-mm)*0.05-eps, MM+(MM-mm)*0.05+eps]);
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else
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% % % % % plot (quartile_y, quartile_x, "b;;",
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% % % % % whisker_y, whisker_x, "b;;",
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% % % % % cap_y, cap_x, "b;;",
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% % % % % median_y, median_x, "r;;",
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% % % % % outliers_y, outliers_x, [symbol(1),"r;;"],
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% % % % % outliers2_y, outliers2_x, [symbol(2),"r;;"]);
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end
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if nargout,
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sout=s;
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end
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% % % endfunction |