### Navigation

- index
- modules |
- next |
- previous |
- SciPy v0.7 Reference Guide (DRAFT) »
- Statistical functions (`scipy.stats`) »

# 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.

### Navigation

- index
- modules |
- next |
- previous |
- SciPy v0.7 Reference Guide (DRAFT) »
- Statistical functions (`scipy.stats`) »