Plotting error bars on curve

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alexandra ligeti 2024-6-20

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2130421-plotting-error-bars-on-curve

评论： Star Strider 2024-6-24

采纳的回答： Star Strider

在 MATLAB Online 中打开

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)')

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Answer 1

Star Strider 2024-6-20

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2130421-plotting-error-bars-on-curve#answer_1474861

在 MATLAB Online 中打开

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

.

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Star Strider 2024-6-24

在 MATLAB Online 中打开

T07b.mat

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

LD = struct with fields:

dat_store: {[1x1000 double] [10x1000 double] [10x1000 double] [10x6 double] [3.9835 3.7506 2.3008 1.0702 1.3536 1.0355 1.1663 0.7576 1.3172 1.4355]}

dat_store = LD.dat_store

dat_store = 1x5 cell array

{1x1000 double} {10x1000 double} {10x1000 double} {10x6 double} {1x10 double}

%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

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)

ans = 1x2 table

var RMSEtable __________ ________________ "alphaIMU" 1.8171 0

var2 = ["alpha"];

CORR = [mean(CORR_IMU) std(CORR_IMU)];

table(var2,CORR)

ans = 1x2 table

var2 CORR _______ __________________ "alpha" 0.99735 0

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))

RMSE_IMUa = 2.1215

%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

I think this is exactly what is happening! Thank you :)

Star Strider 2024-6-24

As always, my pleasure!

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Plotting error bars on curve

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