How can I draw 95% CI plot in my data?
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I have 1 data(100x1 matrix).
In Matlab, I want to draw 95% ci plot in my data
so, I found good code
like this
x = 1:100; % Create Independent Variable
y = randn(50,100); % Create Dependent Variable ‘Experiments’ Data
N = size(y,1); % Number of ‘Experiments’ In Data Set
yMean = mean(y); % Mean Of All Experiments At Each Value Of ‘x’
ySEM = std(y)/sqrt(N); % Compute ‘Standard Error Of The Mean’ Of All Experiments At Each Value Of ‘x’
CI95 = tinv([0.025 0.975], N-1); % Calculate 95% Probability Intervals Of t-Distribution
yCI95 = bsxfun(@times, ySEM, CI95(:)); % Calculate 95% Confidence Intervals Of All Experiments At Each Value Of ‘x’
figure
plot(x, yMean,'p') % Plot Mean Of All Experiments
hold on
plot(x, yCI95+yMean,'-r') % Plot 95% Confidence Intervals Of All Experiments
hold off
grid
but this graph is not what I wanted
How can i draw graph? like this..
Accepted Answer
Star Strider
on 26 Mar 2021
Replace the second plot call with:
patch([x, fliplr(x)], [yCI95(1,:) fliplr(yCI95(2,:))], 'b', 'EdgeColor','none', 'FaceAlpha',0.25)
so the full code is now:
x = 1:100; % Create Independent Variable
y = randn(50,100); % Create Dependent Variable ‘Experiments’ Data
N = size(y,1); % Number of ‘Experiments’ In Data Set
yMean = mean(y); % Mean Of All Experiments At Each Value Of ‘x’
ySEM = std(y)/sqrt(N); % Compute ‘Standard Error Of The Mean’ Of All Experiments At Each Value Of ‘x’
CI95 = tinv([0.025 0.975], N-1); % Calculate 95% Probability Intervals Of t-Distribution
yCI95 = bsxfun(@times, ySEM, CI95(:)); % Calculate 95% Confidence Intervals Of All Experiments At Each Value Of ‘x’
figure
plot(x, yMean,'p') % Plot Mean Of All Experiments
hold on
% plot(x, yCI95+yMean,'-r') % Plot 95% Confidence Intervals Of All Experiments
patch([x, fliplr(x)], [yCI95(1,:) fliplr(yCI95(2,:))], 'b', 'EdgeColor','none', 'FaceAlpha',0.25)
hold off
grid
That should do what you want. (I appreciate your quoting my code!)
1 Comment
Ashish Jadhav
on 10 Apr 2022
The 'y' here is array of 'experiments'. What does this mean? How do we define 'y' in our real single vector data set?
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