hi, Matlab users, by using the following code, I can plot correlation coefficient values against time.
coeffs = [0.54 0.81 0.21 0.61 0.52 0.47 -0.42 -0.20];
T = {'00:00 - 00:27:02', '00:27:02 - 00:35:02', '00:35:02 - 00:47:02', '00:47 - 00:59:55', '01:05:02 - 01:12', '01:15 - 01:25', '01:27 - 01:35', '01:35 - 01:45'};
% split to start and end times
startEndTimes = cellfun(@(str) strsplit(str,' - '),T,'UniformOutput',0);
startTimes = cellfun(@(c) c{1},startEndTimes,'UniformOutput',0);
endTimes = cellfun(@(c) c{2},startEndTimes,'UniformOutput',0);
% add seconds where missing
missingSecondsIdx = cellfun(@length,startTimes) == 5;
startTimes(missingSecondsIdx) = cellfun(@(str) [str ':00'],startTimes(missingSecondsIdx),'UniformOutput',0);
missingSecondsIdx = cellfun(@length,endTimes) == 5;
endTimes(missingSecondsIdx) = cellfun(@(str) [str ':00'],endTimes(missingSecondsIdx),'UniformOutput',0);
% convert time strings to numbers
startTimeNums = datenum(startTimes,'HH:MM:SS');
EPS = 1e-4;
endTimeNums = datenum(endTimes,'HH:MM:SS') - EPS;
% interpolate coefficients on "continous" time vector
contTime = linspace(startTimeNums(1),endTimeNums(end),200);
repCoeffs = repmat(coeffs,[2 1]);
repCoeffs = repCoeffs(:);
allTimes = [startTimeNums';endTimeNums'];
allTimes = allTimes(:);
contCoeffs = interp1(allTimes,repCoeffs,contTime);
% plot
plot(contTime,contCoeffs,'b')
hold on;
plot(startTimeNums,coeffs,'og')
plot(endTimeNums,coeffs,'*r')
datetick('x','HH:MM')
xlabel('TIME [HH:MM]')
ylabel('CORRELATION COEFFICIENTS').
can any one help me how to plot the 95% confidence line and what is needed for this to plot? example is shown in the attach figure. in the attach figure, the spearman correlation coefficient values are above the 95% confidence line for the time interval(20 to 06). What is meant by that the correlation coefficient values are above the 95% confidence level.
If I well remember, you could do that:
figure()
plot(contTime,contCoeffs,'b')
hold on;
plot(startTimeNums,coeffs,'og')
plot(endTimeNums,coeffs,'*r')
datetick('x','HH:MM')
xlabel('TIME [HH:MM]')
ylabel('CORRELATION COEFFICIENTS')
x=contCoeffs;
mx=mean(x);
sx=std(x);
% Approximative
% plot mean
plot([min(contTime),max(contTime)],[mx,mx],'r')
% plot 95%
upper_limit=mx+1.95*sx;
lower_limit=mx-1.95*sx;
plot([min(contTime),max(contTime)],[upper_limit,upper_limit],'r--')
plot([min(contTime),max(contTime)],[lower_limit,lower_limit],'r--')
% plot 99%
upper_limit=mx+1.99*sx;
lower_limit=mx-1.99*sx;
plot([min(contTime),max(contTime)],[upper_limit,upper_limit],'r.-')
plot([min(contTime),max(contTime)],[lower_limit,lower_limit],'r.-')
the 95 and 99 % are computed with the mean and variance of your data.
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