s:
I've found Patrick Happel's answer to not work because the figure window (and therefore the variable b) gets cleared out by subsequent calls to errorbar. Simply adding a hold on command takes care of this. To avoid confusion, here's a new answer that reproduces all of Patrick's original code, plus my small tweak:
%% Old answer %Just to be safe, let's clear everything clear all data = cell(1,10); % Random length of the data l = randi(500, 10, 1) + 50; % Random "width" of the data, with 3 more likely w = randi(4, 10, 1); w(w==4) = 3; % random "direction" of the data d = randi(2, 10, 1); % sigma of the data (in fraction of mean) sigma = rand(10,1) / 3; % means of the data dmean = randi(150,10,1); dsigma = dmean.*sigma; for c = 1 : 10 if d(c) == 1 data{c} = randn(l(c), w(c)) .* dsigma(c) + dmean(c); else data{c} = randn(w(c), l(c)) .* dsigma(c) + dmean(c); end end %============================================ %Next thing is % On the bar, I want it to be shown the median of the values, and I % want to calculate the confidence interval and show it additionally. % %Are you really sure you want to plot the median? The median of some data %is not connected to the variance of the data, and hus no type of error %bars are required. I guess you want to show the mean. If you really want %to show the median, a box plot might be a better alternative. % %The following code computes and plots the mean in a bar plot: %============================================ means = zeros(numel(data),3); stds = zeros(numel(data),3); n = zeros(numel(data),3); for c = 1:numel(data) d = data{c}; if size(d,1) < size(d,2) d = d'; end cols = size(d,2); means(c, 1:cols) = nanmean(d); stds(c, 1:cols) = nanstd(d); n(c, 1:cols) = sum(~isnan((d))); end b = bar(means); %% New code %This ensures that b continues to reference existing data in the next for %loop, as the graphics objects can otherwise be deleted. hold on %% Continuing Patrick Happel's answer %============================================ %Now, we need to compute the length of the error bars. Typical choices are %the standard deviation of the data (already computed by the code above, %stored in stds), the standard error or the 95% confidence interval (which %is the 1.96fold of the standard error, assuming the underlying data %follows a normal distribution). %============================================ % for standard deviation use stds % for standard error ste = stds./sqrt(n); % for 95% confidence interval ci95 = 1.96 * ste; %============================================ %Last thing is to plot the error bars. Here I chose the ci95 as you asked %in your question, if you want to change that, simply change the variable %in the call to errorbar: %============================================ for c = 1:3 size(means(:, c)) size(b(c).XData) e = errorbar(b(c).XData + b(c).XOffset, means(:,c), ci95(:, c)); e.LineStyle = 'none'; end