拟合威布尔与genextreme和weibull_min的分布

使用SciPy，我试图从this question重现威布尔拟合。拟合威布尔与genextreme和weibull_min的分布

import numpy as np 
from scipy.stats import genextreme 
import matplotlib.pyplot as plt 

data=np.array([37.50,46.79,48.30,46.04,43.40,39.25,38.49,49.51,40.38,36.98,40.00, 
       38.49,37.74,47.92,44.53,44.91,44.91,40.00,41.51,47.92,36.98,43.40, 
       42.26,41.89,38.87,43.02,39.25,40.38,42.64,36.98,44.15,44.91,43.40, 
       49.81,38.87,40.00,52.45,53.13,47.92,52.45,44.91,29.54,27.13,35.60, 
       45.34,43.37,54.15,42.77,42.88,44.26,27.14,39.31,24.80,16.62,30.30, 
       36.39,28.60,28.53,35.84,31.10,34.55,52.65,48.81,43.42,52.49,38.00, 
       38.65,34.54,37.70,38.11,43.05,29.95,32.48,24.63,35.33,41.34]) 

shape, loc, scale = genextreme.fit(data) 

plt.hist(data, normed=True, bins=np.linspace(15, 55, 9)) 

x = np.linspace(data.min(), data.max(), 1000) 
y = genextreme.pdf(x, shape, loc, scale) 
plt.plot(x, y, 'c', linewidth=3)

的参数是：(0.44693977076022462, 38.283622522613214, 7.9180988170857374)当我使用genextreme功能如下我的配合看起来不错。形状参数是正的，对应于Weibull wikipedia page上的形状参数的符号，据我所知，它相当于R中的负形状参数。

因此，似乎genextreme自己决定分布是Gumbel，Frechet还是Weibull。这里选择了Weibull。

现在我试图重现与weibull_min函数类似的配合。我曾尝试基于this post以下，但参数看我与genextreme有很大的不同：

weibull_min.fit(data, floc=0)

的参数现在：(6.4633107529634319, 0, 43.247460728065136)

是在0形状参数？如果分布是Weibull，肯定会是正面的？

来源

2016-08-04 Oliver Angelil

无耻插件：paramnormal可能会帮助你在这里：http://phobson.github.io/paramnormal/tutorial/fitting.html –

weibull_min.fit()返回的参数是(shape, loc, scale)。 loc是位置参数。（所有分布SciPy的包括位置参数，甚至那些通常不使用的位置参数。）的weibull_min.fit的文档字符串包括这样的：

Returns 
------- 
shape, loc, scale : tuple of floats 
    MLEs for any shape statistics, followed by those for location and 
    scale.

您使用的参数floc=0，因此，如所预期，位置参数由fit(data, floc=0)返回为0.

来源

2016-08-04 11:48:31

所以我用weibull_min.fit（）得到的形状参数是6.46。这与genextreme的0.44非常不同。并非43的比例参数相当高？如何使用weibull_min.fit（）拟合数据曲线？ –

*关于值：*由weibull_min.fit（data，floc = 0）返回的值与R中fitdistr（mydata，“weibull”）'返回的值非常接近，正如您在链接问题中看到的那样。 –

拟合威布尔与genextreme和weibull_min的分布

回答

相关问题