Python – kappa4 Distribution in Statistics

scipy.stats.kappa4() is an Kappa 4 continuous random variable that is defined with a standard format and some shape parameters to complete its specification. The probability density is defined in the standard form and the loc and scale parameters are used to shift and/or scale the distribution.

Parameters :

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 : kappa4 continuous random variable

Code #1 : Creating kappa4 continuous random variable



filter_none

edit
close

play_arrow

link
brightness_4
code

# importing library
  
from scipy.stats import kappa4  
    
numargs = kappa4.numargs 
a, b = 4.32, 3.18
rv = kappa4(a, b) 
    
print ("RV : \n", rv)  

chevron_right


Output :

RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D50D62C8


Code #2 : Johnson SU continuous variates and probability distribution

filter_none

edit
close

play_arrow

link
brightness_4
code

import numpy as np 
quantile = np.arange (0.01, 1, 0.1
  
# Random Variates 
R = kappa4.rvs(a, b, scale = 2, size = 10
print ("Random Variates : \n", R) 

chevron_right


Output :

Random Variates : 
 [0.62293659 0.62825781 0.62377628 0.62308697 0.62665555 0.62802109
 0.62872844 0.62728058 0.62679381 0.62297679]
 

Code #3 : Graphical Representation.

filter_none

edit
close

play_arrow

link
brightness_4
code

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)) 

chevron_right


Output :

Distribution : 
 [0.         0.06122449 0.12244898 0.18367347 0.24489796 0.30612245
 0.36734694 0.42857143 0.48979592 0.55102041 0.6122449  0.67346939
 0.73469388 0.79591837 0.85714286 0.91836735 0.97959184 1.04081633
 1.10204082 1.16326531 1.2244898  1.28571429 1.34693878 1.40816327
 1.46938776 1.53061224 1.59183673 1.65306122 1.71428571 1.7755102
 1.83673469 1.89795918 1.95918367 2.02040816 2.08163265 2.14285714
 2.20408163 2.26530612 2.32653061 2.3877551  2.44897959 2.51020408
 2.57142857 2.63265306 2.69387755 2.75510204 2.81632653 2.87755102
 2.93877551 3.        ]
 

Code #4 : Varying Positional Arguments

filter_none

edit
close

play_arrow

link
brightness_4
code

import matplotlib.pyplot as plt 
import numpy as np 
     
x = np.linspace(0, 5, 100
     
# Varying positional arguments 
y1 = kappa4 .pdf(x, 1, 3
y2 = kappa4 .pdf(x, 1, 4
plt.plot(x, y1, "*", x, y2, "r--"

chevron_right


Output :

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.




My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.


Article Tags :

Be the First to upvote.


Please write to us at contribute@geeksforgeeks.org to report any issue with the above content.