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# scipy.stats.rdist

`scipy.stats.``rdist` = <scipy.stats.distributions.rdist_gen object at 0x4ad3250>

  An R-distributed 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 c : 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 : 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 = rdist(c, loc=0, scale=1) : Frozen RV object with the same methods but holding the given shape, location, and scale fixed.

  Notes

  The probability density function for `rdist` is:

      rdist.pdf(x, c) = (1-x**2)**(c/2-1) / B(1/2, c/2)

  for `-1 <= x <= 1`, `c > 0`.

  Examples

      >>> from scipy.stats import rdist
      >>> numargs = rdist.numargs
      >>> [ c ] = [0.9,] * numargs
      >>> rv = rdist(c)

  Display frozen pdf

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

  Here, `rv.dist.b` is the right endpoint of the support of `rv.dist`.

  Check accuracy of cdf and ppf

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

  Random number generation

      >>> R = rdist.rvs(c, size=100)

  Methods

  rvs(c, loc=0, scale=1, size=1) | Random variates.
  pdf(x, c, loc=0, scale=1) | Probability density function.
  logpdf(x, c, loc=0, scale=1) | Log of the probability density function.
  cdf(x, c, loc=0, scale=1) | Cumulative density function.
  logcdf(x, c, loc=0, scale=1) | Log of the cumulative density function.
  sf(x, c, loc=0, scale=1) | Survival function (1-cdf — sometimes more accurate).
  logsf(x, c, loc=0, scale=1) | Log of the survival function.
  ppf(q, c, loc=0, scale=1) | Percent point function (inverse of cdf — percentiles).
  isf(q, c, loc=0, scale=1) | Inverse survival function (inverse of sf).
  moment(n, c, loc=0, scale=1) | Non-central moment of order n
  stats(c, loc=0, scale=1, moments=’mv’) | Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
  entropy(c, loc=0, scale=1) | (Differential) entropy of the RV.
  fit(data, c, loc=0, scale=1) | Parameter estimates for generic data.
  expect(func, c, loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) | Expected value of a function (of one argument) with respect to the distribution.
  median(c, loc=0, scale=1) | Median of the distribution.
  mean(c, loc=0, scale=1) | Mean of the distribution.
  var(c, loc=0, scale=1) | Variance of the distribution.
  std(c, loc=0, scale=1) | Standard deviation of the distribution.
  interval(alpha, c, loc=0, scale=1) | Endpoints of the range that contains alpha percent of the distribution

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