Plotting error bars on curve
显示 更早的评论
Hello,
I have code that compares two devices and I am plotting the error on y-axis throughout a gait cycle % (on the x axis).
I am trying to get the error bars to plot the standard error but I am having an unexpected result when plotting the curve.
This is the code I am working with.
Initially x, ave_diff and SE are 1X1000 and I would like to plot it over a gait cycle from 0-100% hence why I have resampled the signals.
However, when I run this code I have extra values at the end of the plot, yet the resmapled variables are all 101X1?
I cannot spot my error and was wondering if someone my point me in the correct direction or suggest where my fault lies.
I have attached the curve and the area where the issue lies, you can see the standard error bars also become strange.
Thanks in advance
figure()
x = smooth(resample((gc),101,length(gc),'Dimension',1));
ave_diff_resample = smooth(resample((ave_diff),101,length(ave_diff),'Dimension',1));
SE_resampled = smooth(resample((SE),101,length(SE),'Dimension',1));
set(gcf,'Color','w');
%standard error plot
plot(x, ave_diff_resample,'-','Color',[0 0 0],'linewidth',0.5)
hold on
% Adding error bars
errorbar(x, ave_diff_resample, SE_resampled, 'vertical');
hold on
hline = refline(0, 0);
hline.Color = 'k';
xlim([0 100]);
ylim([-15 15]);
legend('Signed Difference', 'Standard error bar')
xlabel('Gait cycle %')
ylabel('Difference (deg)')
采纳的回答
I really can’t do anything without the data. The rest of the code (that imports the data and processes it including calculating the standard errors) would be helpful as well. (An image may be worth a thousand words, however the data files and code are worth a thousand images on MATLAB Answers.)
pngs = dir('*.png');
for k = 1:numel(pngs)
figure
imshow(imread(pngs(k).name))
end
.
11 个评论
Star Strider
2024-6-20
Thank you for posting the code.
I really need the data to see what the problem is. We will have to wait until tomorrow.
Alternatively, if you have exhausted your daily limit, did you upload the file to another post? If so, please post the URL for that post.
alexandra ligeti
2024-6-20
Star Strider
2024-6-20
My pleasure!
I will be looking for it.
alexandra ligeti
2024-6-24
Star Strider
2024-6-24
Thank you!
The problem is not obvious from your code. That is the reason at least a representative part of your data would have been helpful.
How large are the data?
alexandra ligeti
2024-6-24
Thank you again!
I do not see any problems with this data set. Am I missing something?
clear all
%File location of Results to analyze
% load ('C:\Users\lexil\Documents\Patient_Study\Healthy Participants\Participant_data/T07b.mat')%young
LD = load('T07b.mat') % Load Into Structure
dat_store = LD.dat_store
%If subjects need to be excluded post hoc, you can list the number here
omits = [];
alphaVicon_GC=[];
alpha_IMU=[];
Vicon_GC=[];
IMU_GC=[];
minV = [];
minIMU = [];
maxV = [];
maxIMU = [];
ROMV = [];
ROMRD = [];
RMSE_IMU = [];
p = [];
diff = [];
std_diff = [];
SE = [];
% dat_store{c,1} = gc; %Gait cycle 0-100
% dat_store{c,2} = alphaVicon_GC; %Vicon
% dat_store{c,3} = alpha1D_GC; %RD
% dat_store{c,4} = alpha_MS_GC;
% dat_store{c,5} = ROM;
% dat_store{c,6} = RMSE;
j=0;
sz = size(dat_store);
for i = 1:sz(1)-length(omits) %
if i == omits
else
ROM_all = dat_store{i,4};
ROMIMU_diff(i,:) = mean(ROM_all(:,1:3))-mean(ROM_all(:,4:6));
ROMIMU(i,:) = mean(ROM_all(:,4:6));
ROMVic(i,:) = mean(ROM_all(:,1:3));
alphaVicon_GC = [alphaVicon_GC;(dat_store{i,2})];
%alpha1D_GC = [alpha1D_GC;dat_store{i,3}];
alpha_IMU = [alpha_IMU;dat_store{i,3}];
% Average gc for each individual
Vicon_GC = [Vicon_GC;mean(dat_store{i,2})];
IMU_GC = [IMU_GC;mean(dat_store{i,3})];
RMSE = dat_store{i,5};
%RMSE_1D = [RMSE_1D ;RMSE(2,:)];
RMSE_IMU = [RMSE_IMU ;RMSE(1,:)];
%CORR_1D(i) = corr(reshape(dat_store{i,2},[],1),reshape(dat_store{i,3},[],1));
CORR_IMU(i) = corr(reshape(dat_store{i,2},[],1),reshape(dat_store{i,3},[],1));
end
end
Vicon_GC = Vicon_GC';
IMU_GC = IMU_GC';
diff = Vicon_GC - IMU_GC;
ave_diff = mean(Vicon_GC') - mean(IMU_GC');
std_diff = std(diff,0,2);
SE = std_diff/sqrt(i);
var = ["alphaIMU"];
RMSEtable = [mean(mean(RMSE_IMU,2)) std(mean(RMSE_IMU,2))];
table(var,RMSEtable)
var2 = ["alpha"];
CORR = [mean(CORR_IMU) std(CORR_IMU)];
table(var2,CORR)
GC_all_IMU = reshape(alphaVicon_GC,[],1)-reshape(alpha_IMU,[],1);
GC_all_IMU(abs(GC_all_IMU)<30);
RMSE_IMUa = sqrt(mean(GC_all_IMU.^2))
%ROM
minV = min(ROM_all(:,1));
minIMU = min(ROM_all(:,4));
maxV = max(ROM_all(:,2));
maxIMU = max(ROM_all(:,5));
minstd_V = std(ROM_all(:,1));
minstd_IMU = std(ROM_all(:,4));
maxstd_V = std(ROM_all(:,2));
maxstd_IMU = std(ROM_all(:,5));
ROMstd_V = std(ROM_all(:,3));
ROMstd_IMU = std(ROM_all(:,6));
ROMV = maxV - minV;
ROMRD = maxIMU - minIMU;
gc = dat_store{1,1};
sig = 1.96; %95% Confidence intervals but if sd then replace with 1
figure
patch([gc,flip(gc)],[mean(alphaVicon_GC)-sig*std(alphaVicon_GC) flip(mean(alphaVicon_GC)+sig*std(alphaVicon_GC)) ],[0 0 0],'facealpha',0.2,'edgealpha',0)
hold on
patch([gc,flip(gc)],[mean(alpha_IMU)-sig*std(alpha_IMU) flip(mean(alpha_IMU)+sig*std(alpha_IMU)) ],[1 0 0],'facealpha',0.1,'edgealpha',0)
%patch([gc,flip(gc)],[mean(alpha1D_GC)-sig*std(alpha1D_GC) flip(mean(alpha1D_GC)+sig*std(alpha1D_GC)) ],[0 0 1],'facealpha',0.1,'edgealpha',0)
plot(gc ,mean(alpha_IMU),'r--','LineWidth',1)
%plot(gc ,mean(alpha1D_GC),'b:','LineWidth',1)
plot(gc, mean(alphaVicon_GC),'k-','LineWidth',1)
%xlim([0 TimeEnd])
ylim([-20 80])
grid on
ylabel('Knee flexion angle (\circ)')
xlabel('Gait Cycle (%)')
xticklabels(["0%","20%","40%","60%","80%","100%"])
% g(1) = patch(NaN,NaN,[1 0 0],'facealpha',0.2);
% g(2) = patch(NaN,NaN,[0 0 0],'facealpha',0.2);
% g(3) = patch(NaN,NaN,[0 0 1],'facealpha',0.2);
g(1) = plot(NaN,NaN,'k-','LineWidth',1);
g(2) = plot(NaN,NaN,'r--','LineWidth',1);
rgb = [0 0 0];
FaceAlpha = (0.1);
g(3) = patch([NaN],[NaN],rgb,'EdgeAlpha', 0, 'FaceAlpha',FaceAlpha);
legend(g,'Camera-Marker','IMU','± 1.96*SD','location','best')%'northwest')
figure()
subplot(3,1,1)
plot(gc ,mean(alpha_IMU),'k--','LineWidth',1)
hold on
plot(gc, mean(alphaVicon_GC),'k-','LineWidth',1)
legend('IMU','Vicon')
xlabel('Gait cycle %')
ylabel('Knee Angle (deg)')
ylim([-20 80]);
grid on
subplot(3,1,2)
plot(gc, (mean(alphaVicon_GC)-mean(alpha_IMU)),'k-','LineWidth',1)
legend('Signed difference')
xlabel('Gait cycle %')
ylabel('Difference (deg)')
ylim([-10 10]);
grid on
subplot(3,1,3)
plot(gc, abs(mean(alphaVicon_GC)-mean(alpha_IMU)),'k-','LineWidth',1)
legend('Absolute difference')
xlabel('Gait cycle %')
ylabel('Difference (deg)')
ylim([-10 10]);
grid on
figure()
x = smooth(resample((gc),101,length(gc),'Dimension',1));
ave_diff_resample = smooth(resample((ave_diff),101,length(ave_diff),'Dimension',1));
SE_resampled = smooth(resample((SE),101,length(SE),'Dimension',1));
set(gcf,'Color','w');
%standard error plot
plot(x, ave_diff_resample,'-','Color',[0 0 0],'linewidth',0.5)
hold on
% Adding error bars
errorbar(x, ave_diff_resample, SE_resampled, 'vertical');
hold on
% hline = refline(0, 0);
% hline.Color = 'k';
yline(0, 'k')
xlim([0 100]);
ylim([-15 15]);
legend('Signed Difference', 'Standard error bar')
xlabel('Gait cycle %')
ylabel('Difference (deg)')
%% Average Bland-Altman plot
% Plot mean difference line
figure()
set(gcf,'Color','w');
ave_vic = mean(Vicon_GC');
ave_imu = mean(IMU_GC');
mean_data = (ave_vic + ave_imu)/2;
mean_data = resample((mean_data),101,length(mean_data),'Dimension',1);
std_diff = resample((std_diff),101,length(std_diff),'Dimension',1);
first_index = 1;
last_index = numel(mean_data);
scatter(mean_data(first_index), ave_diff_resample(first_index),80, '*');
hold on
scatter(mean_data(last_index), ave_diff_resample(last_index),80, '^');
hold on
scatter(mean_data(2:100), ave_diff_resample(2:100), 'filled','MarkerFaceColor', 'black');
hold on;
%+- 2SD
plot([0 120], [mean(ave_diff_resample)'+ 2*mean(std_diff), mean(ave_diff_resample)' + 2*mean(std_diff)], 'r--');
plot([0 120], [mean(ave_diff_resample)' - 2*mean(std_diff), mean(ave_diff_resample)' - 2*mean(std_diff)], 'r--');
%mean diff line
plot([0 120], [mean(ave_diff_resample), mean(ave_diff_resample)], 'k-', 'LineWidth', 1);
%calculate error bars
%error bars are the standard error of the sample
error_bar_y = errorbar(mean_data,ave_diff_resample,SE_resampled, 'vertical', 'LineStyle', 'none');
set(error_bar_y, 'color', 'k', 'LineWidth', 0.5)
legend({'HS', 'HS', 'Average gait cycle Data', '+ 2 SD', '- 2 SD', 'Mean Difference', 'SE'});
hold on
xlim([0 120])
ylim([-10 10])
xlabel('Mean($\o - \bar{\o}$) (deg)', 'Interpreter','Latex')
ylabel('Difference (deg)')
title('Cycling - Younger Adults')
.
alexandra ligeti
2024-6-24
移动：Star Strider
2024-6-24
My guess (and it only a guess) is that the reason could be that the data do not always have common independent vairable values. This can lead to the ends being different, even if the independent vairable values all begin at the same point (for example, 0). One fix for this could be to interpolate all the dependent variable values to the shortest independent variable, most likely using the interp1 function.
An example —
x1 = linspace(0, 0.9, 25).';
y1 = sin(2*pi*x1/max(x1));
x2 = linspace(0, 1.1, 28).';
y2 = sin(2*pi*x2/max(x2));
x3 = linspace(0, 1, 27).';
y3 = sin(2*pi*x3/max(x3));
figure
plot(x1, y1)
hold on
plot(x2, y2)
plot(x3, y3)
hold off
grid
xl = xlim;
title('Original')
y2i = interp1(x2, y2, x1);
y3i = interp1(x3, y3, x1);
figure
plot(x1, y1)
hold on
plot(x1, y2i)
plot(x1, y3i)
hold off
grid
xlim(xl)
title('Interpolated')
If this is the problem, then interpolating could be tthe solution.
.
alexandra ligeti
2024-6-24
Star Strider
2024-6-24
As always, my pleasure!
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