Python – 统计中的非中心 F 分布
scipy.stats.ncf()是一个非中心 F 分布的连续随机变量。它作为rv_continuous 类的实例继承自泛型方法。它使用特定于此特定发行版的详细信息来完成方法。
参数 :
q : lower and upper tail probability
x : quantiles
loc : [optional]location parameter. Default = 0
scale : [optional]scale parameter. Default = 1
size : [tuple of ints, optional] shape or random variates.
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).
Results : non-central F distribution continuous random variable
代码 #1:创建非中心 F 分布连续随机变量
# importing library
from scipy.stats import ncf
numargs = ncf.numargs
a, b = 4.32, 3.18
rv = ncf(a, b)
print ("RV : \n", rv)
输出 :
RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D7FA4AC8
代码 #2:非中心 F 分布连续变量和概率分布
import numpy as np
quantile = np.arange (0.01, 1, 0.1)
# Random Variates
R = ncf.rvs(a, b)
print ("Random Variates : \n", R)
# PDF
R = ncf.pdf(a, b, quantile)
print ("\nProbability Distribution : \n", R)
输出 :
Random Variates :
4.491293530275984
Probability Distribution :
[0.03182343 0.03305119 0.03427159 0.03548422 0.03668876 0.0378848
0.03907367 0.04025304 0.04142343 0.04258456]
代码#3:图形表示。
import numpy as np
import matplotlib.pyplot as plt
distribution = np.linspace(0, np.minimum(rv.dist.b, 3))
print("Distribution : \n", distribution)
plot = plt.plot(distribution, rv.pdf(distribution))
输出 :
Distribution :
[0. 0.04081633 0.08163265 0.12244898 0.16326531 0.20408163
0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959
0.48979592 0.53061224 0.57142857 0.6122449 0.65306122 0.69387755
0.73469388 0.7755102 0.81632653 0.85714286 0.89795918 0.93877551
0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347
1.2244898 1.26530612 1.30612245 1.34693878 1.3877551 1.42857143
1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939
1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735
1.95918367 2. ]
代码#4:改变位置参数
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 5, 100)
# Varying positional arguments
y1 = ncf .pdf(x, 1, 3)
y2 = ncf .pdf(x, 1, 4)
plt.plot(x, y1, "*", x, y2, "r--")
输出 :