scipy.stats.johnsonsu

scipy.stats.johnsonsu()

A Johnson SU continuous random variable.

Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:

Parameters:

x : array-like

quantiles

q : array-like

lower or upper tail probability

a,b : array-like

shape parameters

loc : array-like, optional

location parameter (default=0)

scale : array-like, optional

scale parameter (default=1)

size : int or tuple of ints, optional

shape of random variates (default computed from input arguments )

moments : string, optional

composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. (default=’mv’)

Methods:

johnsonsu.rvs(a,b,loc=0,scale=1,size=1) :

• random variates

johnsonsu.pdf(x,a,b,loc=0,scale=1) :

• probability density function

johnsonsu.cdf(x,a,b,loc=0,scale=1) :

• cumulative density function

johnsonsu.sf(x,a,b,loc=0,scale=1) :

• survival function (1-cdf — sometimes more accurate)

johnsonsu.ppf(q,a,b,loc=0,scale=1) :

• percent point function (inverse of cdf — percentiles)

johnsonsu.isf(q,a,b,loc=0,scale=1) :

• inverse survival function (inverse of sf)

johnsonsu.stats(a,b,loc=0,scale=1,moments=’mv’) :

• mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’)

johnsonsu.entropy(a,b,loc=0,scale=1) :

• (differential) entropy of the RV.

johnsonsu.fit(data,a,b,loc=0,scale=1) :

• Parameter estimates for johnsonsu data

Alternatively, the object may be called (as a function) to fix the shape, :

location, and scale parameters returning a “frozen” continuous RV object: :

rv = johnsonsu(a,b,loc=0,scale=1) :

• frozen RV object with the same methods but holding the given shape, location, and scale fixed

Examples

>>> import matplotlib.pyplot as plt
>>> numargs = johnsonsu.numargs
>>> [ a,b ] = [0.9,]*numargs
>>> rv = johnsonsu(a,b)

Display frozen pdf

>>> x = np.linspace(0,np.minimum(rv.dist.b,3))
>>> h=plt.plot(x,rv.pdf(x))

Check accuracy of cdf and ppf

>>> prb = johnsonsu.cdf(x,a,b)
>>> h=plt.semilogy(np.abs(x-johnsonsu.ppf(prb,c))+1e-20)

Random number generation

>>> R = johnsonsu.rvs(a,b,size=100)

Johnson SU distribution

johnsonsu.pdf(x,a,b) = b/sqrt(x**2+1) * phi(a + b*log(x+sqrt(x**2+1))) for all x, a,b > 0, and phi is the normal pdf.