standard deviation on power vs. frequency graph

Question

nines 2020-1-18

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/500882-standard-deviation-on-power-vs-frequency-graph

回答： Star Strider 2020-1-18

采纳的回答： Star Strider

在 MATLAB Online 中打开

Hello all!

I am trying to put a shaded standard deviation on a power vs. frequency graph.

I've tried:

%errorbar
plot(f, mean(allspectrums_cortex_young)); hold on;  
errorbar(f, std(allspectrums_cortex_young)/sqrt(13)); hold on;
fill(f, std(allspectrums_cortex_young)/sqrt(13),[1 0 0],'LineStyle','none');
title('Young Power - Cortex')
ylim([0 2000])
xlim([0 .15])
xlabel('Frequency (in hertz)');
ylabel('Power (n=13)'
%fill
plot(f, mean(allspectrums_cortex_young)); hold on;  
fill(f, std(allspectrums_cortex_young)/sqrt(13),[1 0 0],'LineStyle','none');
title('Young Power - Cortex')
ylim([0 2000])
xlim([0 .15])
xlabel('Frequency (in hertz)');
ylabel('Power (n=13)'

and I get this:

Screen Shot 2020-01-18 at 11.11.36 AM.png

when I am looking for something more like:

Screen Shot 2020-01-18 at 11.13.30 AM.png

does anyone have any ideas? thanks so so much!

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Star Strider 2020-1-18

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/500882-standard-deviation-on-power-vs-frequency-graph#answer_410781

在 MATLAB Online 中打开

It would help to have your data, however I created some to illustrate the approach:

f = linspace(0, 0.5, 25);                                           % Frequency Vector
allspectrums_cortex_young = 1E+3*rand(5, 25) + 1600*exp(-10*f);     % Synthetic Spectrum
mn_allspectrums_cortex_young = mean(allspectrums_cortex_young);
sd_allspectrums_cortex_young = std(allspectrums_cortex_young);
figure
plot(f, mn_allspectrums_cortex_young)
hold on
patch([f fliplr(f)], [(mn_allspectrums_cortex_young+sd_allspectrums_cortex_young) fliplr(mn_allspectrums_cortex_young-sd_allspectrums_cortex_young)], 'r', 'FaceAlpha',0.5, 'EdgeColor','none')
hold off

The approach (using the patch function) is to create a closed curve that patch then fills. That is done here by adding and then subtracting the std from the mean, after plotting the mean.

Example —