使用DistFit（）时，是否有一种方法可以绘制其他分布的方法？
我正在使用distFit（）将我的数据适合最佳分布。结果使我的genextreme为0.00045，RSS为0.00048，RSS为0.00045。现在，我的教授说，使用genextreme并将这种用途作为“工具告诉我”的合理性是不够的。现在，我想使用Loggamma发行版，因为它也非常接近（并且更容易证明）。使用函数dist.plot（）时，它只是绘制最佳性能分布。
无论如何，我能得到它来绘制我选择的分布吗？
我使用的代码如下（但我无法在受保护的情况下添加数据）。
# Initialize distfit
dist = distfit()
# Determine best-fitting probability distribution for data
dist .fit_transform(X)
# Print summary of evaluated distributions
print(dist .summary)
dist .plot()
I am using distfit() to fit my data to the best distribution. The results give me genextreme with and RSS of 0.00045 and loggamma with an RSS of 0.00048. Now my professor said that using genextreme and justifying that use as "the tools told me so" is not really good enough. Now I want to use the loggamma distribution as it is quite close as well (and easier to justify). When using the function dist.plot() it just plots the best performing distribution.
Is there anyway I can get it to plot a distribution of my choice?
The code I use is as follows (but I cant add my data as it is protected).
# Initialize distfit
dist = distfit()
# Determine best-fitting probability distribution for data
dist .fit_transform(X)
# Print summary of evaluated distributions
print(dist .summary)
dist .plot()
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您只能为您要寻找的
dist = dist fit = ['loggamma']重新计算区域
you can recompute the dist only for the one you are looking for
dist = distfit(distr=['loggamma'])