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# scipy.stats.logistic

`scipy.stats.``logistic`

  A logistic 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 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 : str, 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’) Alternatively, the object may be called (as a function) to fix the shape, : location, and scale parameters returning a “frozen” continuous RV object: : rv = logistic(loc=0, scale=1) : Frozen RV object with the same methods but holding the given shape, location, and scale fixed.

  Notes

  Logistic distribution

  logistic.pdf(x) = exp(-x)/(1+exp(-x))**2

  Examples

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

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

  Random number generation

      >>> R = logistic.rvs(size=100)

  (Source code)

  Methods

  rvs(loc=0, scale=1, size=1) | Random variates.
  pdf(x, loc=0, scale=1) | Probability density function.
  cdf(x, loc=0, scale=1) | Cumulative density function.
  sf(x, loc=0, scale=1) | Survival function (1-cdf — sometimes more accurate).
  ppf(q, loc=0, scale=1) | Percent point function (inverse of cdf — percentiles).
  isf(q, loc=0, scale=1) | Inverse survival function (inverse of sf).
  stats(loc=0, scale=1, moments=’mv’) | Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
  entropy(loc=0, scale=1) | (Differential) entropy of the RV.
  fit(data, loc=0, scale=1) | Parameter estimates for generic data.

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