scipy.stats.tukeylambda = <scipy.stats._continuous_distns.tukeylambda_gen object at 0x2b45d3029550>[source]

A Tukey-Lamdba 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:


x : array_like


q : array_like

lower or upper tail probability

lam : 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 = tukeylambda(lam, loc=0, scale=1)

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


A flexible distribution, able to represent and interpolate between the following distributions:

  • Cauchy (lam=-1)
  • logistic (lam=0.0)
  • approx Normal (lam=0.14)
  • u-shape (lam = 0.5)
  • uniform from -1 to 1 (lam = 1)


>>> from scipy.stats import tukeylambda
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)

Calculate a few first moments:

>>> lam = 3.13214778567
>>> mean, var, skew, kurt = tukeylambda.stats(lam, moments='mvsk')

Display the probability density function (pdf):

>>> x = np.linspace(tukeylambda.ppf(0.01, lam),
...               tukeylambda.ppf(0.99, lam), 100)
>>> ax.plot(x, tukeylambda.pdf(x, lam),
...          'r-', lw=5, alpha=0.6, label='tukeylambda pdf')

Alternatively, freeze the distribution and display the frozen pdf:

>>> rv = tukeylambda(lam)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of cdf and ppf:

>>> vals = tukeylambda.ppf([0.001, 0.5, 0.999], lam)
>>> np.allclose([0.001, 0.5, 0.999], tukeylambda.cdf(vals, lam))

Generate random numbers:

>>> r = tukeylambda.rvs(lam, size=1000)

And compare the histogram:

>>> ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)

(Source code)



rvs(lam, loc=0, scale=1, size=1) Random variates.
pdf(x, lam, loc=0, scale=1) Probability density function.
logpdf(x, lam, loc=0, scale=1) Log of the probability density function.
cdf(x, lam, loc=0, scale=1) Cumulative density function.
logcdf(x, lam, loc=0, scale=1) Log of the cumulative density function.
sf(x, lam, loc=0, scale=1) Survival function (1-cdf — sometimes more accurate).
logsf(x, lam, loc=0, scale=1) Log of the survival function.
ppf(q, lam, loc=0, scale=1) Percent point function (inverse of cdf — percentiles).
isf(q, lam, loc=0, scale=1) Inverse survival function (inverse of sf).
moment(n, lam, loc=0, scale=1) Non-central moment of order n
stats(lam, loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(lam, loc=0, scale=1) (Differential) entropy of the RV.
fit(data, lam, loc=0, scale=1) Parameter estimates for generic data.
expect(func, lam, 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(lam, loc=0, scale=1) Median of the distribution.
mean(lam, loc=0, scale=1) Mean of the distribution.
var(lam, loc=0, scale=1) Variance of the distribution.
std(lam, loc=0, scale=1) Standard deviation of the distribution.
interval(alpha, lam, loc=0, scale=1) Endpoints of the range that contains alpha percent of the distribution

Previous topic


Next topic