1.https://www.ilovematlab.cn/thread-165903-1-1.html 使用面积函数
% 画出误差阴影。
clear;clc;close all;
dat = [0.4945,0.5427,0.5598,0.5333,0.5474,0.4697,0.5231,0.4390;
0.5667,0.6384,0.5139,0.5000,0.5138,0.6087,0.5800,0.5824;
0.5546,0.6148,0.5548,0.5312,0.5493,0.5877,0.5000,0.5800;
0.6158,0.6386,0.6025,0.4988,0.6000,0.6706,0.6300,0.4824;
0.6875,0.5299,0.6000,0.5865,0.6500,0.7121,0.5607,0.5354;
0.5979,0.7516,0.6111,0.6041,0.6589,0.7297,0.7059,0.7151;
0.6875,0.7371,0.7409,0.7571,0.6176,0.6406,0.6800,0.6987;
0.6667,0.8229,0.6958,0.6420,0.7119,0.6557,0.6652,0.7561;
0.6891,0.7083,0.8915,0.7047,0.8207,0.6953,0.6652,0.6667;
0.8667,0.8438,0.8489,0.7977,0.7460,0.8814,0.7727,0.8815;
0.9231,0.8100,0.9263,0.9698,0.9370,0.9500,0.8670,0.9000];
fh=figure(1);clf;
y = (mean(dat'))';
e = (std(dat'))';
x = (1:length(y))';
h = area(x,[y - e, 2 * e]);hold on;
set(h(1),'Visible','off');
set(h(2),'EdgeColor','white','FaceColor',[0.7,0.7,1]);
plot(x,y,'-b','LineWidth',2);
xlabel('Time(day)');
set(gca,'YTick',[0,0.25,0.50,0.75,1.00]);
ylim([0,1]);
ylabel('Succuss Rate (%)');
求出每一行的均值和标准差，用曲线图画出均值，用面积图画出标准差阴影。
2. 使用shadedErrorBar
https://blog.csdn.net/zzx2016zzx/article/details/84000948
shadedErrorBar是一个MATLAB函数，它在一条线而不是离散条上创建一个连续的阴影误差区域。可以显式指定错误区域，也可以根据函数句柄动态计算错误区域。可以通过输入参数和/或返回的绘图对象的句柄来修改绘图的外观。
1、首先准备下载shadedErrorBar.m函数代码，mathworks官网下载或者github
在此我给出已经下载的代码
function varargout=shadedErrorBar(x,y,errBar,varargin)
% generate continuous error bar area around a line plot
% function H=shadedErrorBar(x,y,errBar, ...)
% Purpose
% Makes a 2-d line plot with a pretty shaded error bar made
% using patch. Error bar color is chosen automatically.
% Inputs (required)
% x - vector of x values [optional, can be left empty]
% y - vector of y values or a matrix of n observations by m cases
% where m has length(x);
% errBar - if a vector we draw symmetric errorbars. If it has a size
% of [2,length(x)] then we draw asymmetric error bars with
% row 1 being the upper bar and row 2 being the lower bar
% (with respect to y -- see demo). ** alternatively **
% errBar can be a cellArray of two function handles. The
% first defines statistic the line should be and the second
% defines the error bar.
% Inputs (optional, param/value pairs)
% 'lineProps' - ['-k' by default] defines the properties of
% the data line. e.g.:
% 'or-', or {'-or','markerfacecolor',[1,0.2,0.2]}
% 'transparent' - [true by default] if true, the shaded error
% bar is made transparent. However, for a transparent
% vector image you will need to save as PDF, not EPS,
% and set the figure renderer to "painters". An EPS
% will only be transparent if you set the renderer
% to OpenGL, however this makes a raster image.
% 'patchSaturation'- [0.2 by default] The saturation of the patch color.
% Outputs
% H - a structure of handles to the generated plot objects.
% Examples:
% y=randn(30,80);
% x=1:size(y,2);
% 1)
% shadedErrorBar(x,mean(y,1),std(y),'lineprops','g');
% 2)
% shadedErrorBar(x,y,{@median,@std},'lineprops',{'r-o','markerfacecolor','r'});
%
% 3)
% shadedErrorBar([],y,{@median,@(x) std(x)*1.96},'lineprops',{'r-o','markerfacecolor','k'});
% 4)
% Overlay two transparent lines:
% clf
% y=randn(30,80)*10;
% x=(1:size(y,2))-40;
% shadedErrorBar(x,y,{@mean,@std},'lineprops','-r','transparent',1);
% hold on
% y=ones(30,1)*x; y=y+0.06*y.^2+randn(size(y))*10;
% shadedErrorBar(x,y,{@mean,@std},'lineprops','-b','transparent',1);
% hold off
%
%
% Rob Campbell - November 2009
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Parse input arguments
narginchk(3,inf)
params = inputParser;
params.CaseSensitive = false;
params.addParameter('lineProps', '-k', @(x) ischar(x) | iscell(x));
params.addParameter('transparent', true, @(x) islogical(x) || x==0 || x==1);
params.addParameter('patchSaturation', 0.2, @(x) isnumeric(x) && x>=0 && x<=1);
params.parse(varargin{:});
%Extract values from the inputParser
lineProps = params.Results.lineProps;
transparent = params.Results.transparent;
patchSaturation = params.Results.patchSaturation;
if ~iscell(lineProps), lineProps={lineProps}; end
%Process y using function handles if needed to make the error bar dynamically
if iscell(errBar)
fun1=errBar{1};
fun2=errBar{2};
errBar=fun2(y);
y=fun1(y);
else
y=y(:).';
end
if isempty(x)
x=1:length(y);
else
x=x(:).';
end
%Make upper and lower error bars if only one was specified
if length(errBar)==length(errBar(:))
errBar=repmat(errBar(:)',2,1);
else
s=size(errBar);
f=find(s==2);
if isempty(f), error('errBar has the wrong size'), end
if f==2, errBar=errBar'; end
end
if length(x) ~= length(errBar)
error('length(x) must equal length(errBar)')
end
%Log the hold status so we don't change
initialHoldStatus=ishold;
if ~initialHoldStatus, hold on, end
H = makePlot(x,y,errBar,lineProps,transparent,patchSaturation);
if ~initialHoldStatus, hold off, end
if nargout==1
varargout{1}=H;
end
function H = makePlot(x,y,errBar,lineProps,transparent,patchSaturation)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Plot to get the parameters of the line
H.mainLine=plot(x,y,lineProps{:});
% Work out the color of the shaded region and associated lines.
% Here we have the option of choosing alpha or a de-saturated
% solid colour for the patch surface.
mainLineColor=get(H.mainLine,'color');
edgeColor=mainLineColor+(1-mainLineColor)*0.55;
if transparent
faceAlpha=patchSaturation;
patchColor=mainLineColor;
else
faceAlpha=1;
patchColor=mainLineColor+(1-mainLineColor)*(1-patchSaturation);
end
%Calculate the error bars
uE=y+errBar(1,:);
lE=y-errBar(2,:);
%Add the patch error bar
%Make the patch
yP=[lE,fliplr(uE)];
xP=[x,fliplr(x)];
%remove nans otherwise patch won't work
xP(isnan(yP))=[];
yP(isnan(yP))=[];
if(isdatetime(x))
H.patch=patch(datenum(xP),yP,1);
else
H.patch=patch(xP,yP,1);
end
set(H.patch,'facecolor',patchColor, ...
'edgecolor','none', ...
'facealpha',faceAlpha)
%Make pretty edges around the patch.
H.edge(1)=plot(x,lE,'-','color',edgeColor);
H.edge(2)=plot(x,uE,'-','color',edgeColor);
uistack(H.mainLine,'top') % Bring the main line to the top
2、选择shadedErrorBar.m文件黏贴到matalb的toolbox文件下面，我设置的路径是D:\Program Files\MATLAB\R2016b\toolbox\shadedErrorBaryy。然后选择matlab主页的设置路径
3、将预先设置的文件添加进去
4、检查是否设置成功
在matlab运行窗口输入help shadedErrorBar，如果出现以下情况说明 shadedErrorBar函数设置成功
5、最后运行
% Prepare data
y=randn(30,80)*5;
x=(1:size(y,2))-40;
yP = sin( linspace(-2*pi,2*pi,length(x)) )*20;
y = bsxfun(@plus,y,yP)+60;
% Make the plot
clf
shadedErrorBar(x,y,{@mean,@std});
% Overlay the raw data
hold on
plot(x,y,'.','color',[0.5,0.5,0.95])
grid on
% Prepare data for first line
y=ones(30,1)*x;
y=y+0.06*y.^2+randn(size(y))*10;
clf
shadedErrorBar(x,y,{@mean,@std},'lineprops','-b','patchSaturation',0.33)
% Overlay second line
hold on
shadedErrorBar(x,2*y+20,{@mean,@std},'lineprops',{'-go','MarkerFaceColor','g'});
%Overlay third line
y=randn(30,80)*5;
x=(1:size(y,2))-40;
yP = sin( linspace(-2*pi,2*pi,length(x)) )*20;
y = bsxfun(@plus,y,yP)+60;
% Make this line non-transparent
shadedErrorBar(x, y, {@mean,@std}, 'lineprops', '-r','transparent',false,'patchSaturation',0.075)
grid on
% Prepare data
y=randn(30,80)*5;
x=(1:size(y,2));
yP = sin( linspace(-2*pi,2*pi,length(x)) )*20;
y = bsxfun(@plus,y,yP);
% Plot
clf
s = shadedErrorBar(x, y, {@mean,@std}, 'lineprops', '-r');
% Set face and edge properties
set(s.edge,'LineWidth',2,'LineStyle',':')
s.mainLine.LineWidth = 5;
s.patch.FaceColor = [0.5,0.25,0.25];
% Overlay data points post-hoc
hold on
plot(s.mainLine.XData, s.mainLine.YData,'or','MarkerFaceColor','w')
grid on
set(gca,'XTickLabel',[],'YTickLabel',[])
% Post-hoc modifications of line properties
y=randn(256,80)*5;
x=(1:size(y,2));
yP = cos( linspace(-2*pi,2*pi,length(x)) )*10;
y = bsxfun(@plus,y,yP);
shadedErrorBar(x, y, {@mean,@std}, 'lineprops', '-r')
hold on
y=mean(y)+16;
errbar = [2*ones(1,length(x)) ; 4*ones(1,length(x))];
shadedErrorBar(x, y, errbar, 'lineprops', '-g')
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原文链接：https://blog.csdn.net/zzx2016zzx/article/details/84000948
