我正在努力使用 PGFplots 创建具有叠加正态（高斯）曲线的直方图。更具体地说，我想重新创建此 SPSS 图：
使用 PGFplots 创建直方图对我来说没有问题。图如下所示：
上面这个图的代码是：
% Gauss function, parameters mu and sigma
\newcommand\gauss[2]{1/(#2*sqrt(2*pi))*exp(-((x-#1)^2)/(2*#2^2))}
% Histogram
\begin{tikzpicture}
\begin{axis}[
height=7cm,
width=6cm,
xmin=0.67,
xmax=0.75,
]
\addplot[
black,
fill=lightgray,
hist,
hist/bins=20,
] table[
y=true-error,
] {Data/compare-cv-normality-1002-error.dat};
%\addplot {\gauss{0.71}{0.00944}};
\end{axis}
\end{tikzpicture}
如您所见，我已经插入了高斯图的代码，但由于它会产生以下错误而被注释：
Dimension too large. ^^I^^I\addplot {\gauss{0.71}{0.00944}};
Arithmetic overflow. ^^I^^I\addplot {\gauss{0.71}{0.00944}};
另外，第二个问题是添加高斯会将 X 轴设置为 (-5, 5) 范围，我必须根据直方图数据手动重新调整它。到目前为止，这是我最不优先考虑的问题，因为解决方案相当简单。但如果您可以在不设置xmin和的情况下解决这个问题xmax，我将不胜感激。
数据文件的内容：
true-error
0.6949672084402624
0.7182777302537782
0.7026660963786713
0.7184915882520673
0.7052323923581408
0.7164955802680354
0.7028086683775306
0.6941117764471058
0.7085828343313373
0.7038779583689764
0.7040205303678357
0.7072996863416026
0.6899059024807528
0.7060878243512974
0.7093669803250642
0.7167094382663245
0.7295409181636726
0.7264043341887654
0.7033076703735386
0.7170658682634731
0.7255489021956087
0.7386655260906758
0.7051611063587111
0.708654120330767
0.6983889364128885
0.7140718562874252
0.6916880524664956
0.6906187624750499
0.7093669803250642
0.6945394924436841
0.7037353863701169
0.7087254063301968
0.7050185343598517
0.7074422583404619
0.6953236384374109
0.721271742229826
0.7040205303678357
0.7088679783290561
0.710151126318791
0.7279726261762189
0.6976047904191617
0.7176361562589108
0.7006700883946393
0.7020245223838039
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0.6995295124037639
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0.714357000285144
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0.7085115483319077
0.7072284003421728
0.7236954662104362
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0.6976047904191617
0.7201311662389507
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答案1
如果没有相反的明确指示，pgfplots则试图在您感兴趣的域之外评估高斯方式，由于指数的参数变大而导致算术错误。
\addplot[domain={0.67:0.75}]{\gauss{0.71}{0.00944}};
就可以了（当然，为了匹配 SPSS 图，您可能还想在高斯中包含一个比例参数）。
由HK编辑
除了域之外，为了使高斯的峰值与条形相匹配，您可以使用ysacle=4.25。添加samples=150使高斯更平滑。
\addplot[domain={0.67:0.75},yscale=4.25,samples=150] {\gauss{0.71}{0.00944}};
给出
由 alesc 编辑
缩放因子通过以下公式计算：
0.997 * num_samples * (xmax - xmin) / num_bins
假设xmax和xmin是通过 +-3 sigma 规则计算的。这也是将方程与 相乘的原因0.997。
梅威瑟：带有 x,y 标签
\documentclass[border=3mm,tikz,preview]{standalone}
\usepackage{pgfplots}
\newcommand\gauss[2]{1/(#2*sqrt(2*pi))*exp(-((x-#1)^2)/(2*#2^2))}
\begin{document}
\begin{tikzpicture}
\begin{axis}[
height=7cm,
width=6cm,
xmin=0.67,
xmax=0.75,
xlabel = truerror,
ylabel = Frequency
]
\addplot[
black,
fill=lightgray,
hist,
hist/bins=20,
] table[
y=true-error,
] {error.dat};
\addplot[domain={0.67:0.75},yscale=4.25,samples=150] {\gauss{0.71}{0.00944}};
\end{axis}
\end{tikzpicture}
\end{document}
