Hi guys!
How can I merge multiple lines into one on the same plot? I have plotted the graphs, what command or tool i should use to combine them? Function for the plot: plot(r1x,r1y,r2x,r2y,r3x,r3y).
Many thanks!
MATLAB: How to merge multiple graphs in a same plot
plotting
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In order to calculate the 95% confidence intervals of your signal, you first will need to calculate the mean and *|std| (standard deviation) of your experiments at each value of your independent variable. The standard way to do this is to calculate the standard error of the mean at each value of your independent variable, multiply it by the calculated 95% values of the t-distribution (here), then add and subtract those values from the mean. The plot is then straightforward. (The tinv function is in the Statistics and Machine Learning Toolbox.)
Example —
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’
figureplot(x, yMean) % Plot Mean Of All Experiments
hold onplot(x, yCI95+yMean) % Plot 95% Confidence Intervals Of All Experiments
hold offgridThis should get you started.
This enhancement has been incorporated in Release 2006a (R2006a). For previous product releases, read below for any possible workarounds:
There is a well-defined relation between the width of the confidence interval in standard errors and the percentage of observations falling within the confidence interval. To calculate the standard error, first you will need to find the confidence interval. Then you can use the TINV function to calculate the number of standard errors spanned by the confidence interval. Finally, divide the confidence interval by the number of standard errors spanned by the confidence interval to find the standard error.
Here is an illustration of how to compute the standard error:
1. Fit a model using the NLINFIT function, and get the parameters.
load hald y = heat; x = ingredients(:,4); [b,r,J] = nlinfit(y,x,@(b,x) b(1)+b(2)*x, 1:2);2. Estimate the confidence interval using the NLPARCI function:
alpha = 0.05ci = nlparci(b,r,J,alpha); % 95% confidence intervals
3. Compute the standard error from the confidence interval using the TINV function:
t = tinv(1-alpha/2,length(y)-length(b)); nlinfit_se = (ci(:,2)-ci(:,1)) ./ (2*t) % Standard Error
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