Module « scipy.stats »
Signature de la fonction nchypergeom_wallenius
def nchypergeom_wallenius(*args, **kwds)
Description
nchypergeom_wallenius.__doc__
A Wallenius' noncentral hypergeometric discrete random variable.
Wallenius' noncentral hypergeometric distribution models drawing objects of
two types from a bin. `M` is the total number of objects, `n` is the
number of Type I objects, and `odds` is the odds ratio: the odds of
selecting a Type I object rather than a Type II object when there is only
one object of each type.
The random variate represents the number of Type I objects drawn if we
draw a pre-determined `N` objects from a bin one by one.
As an instance of the `rv_discrete` class, `nchypergeom_wallenius` object inherits from it
a collection of generic methods (see below for the full list),
and completes them with details specific for this particular distribution.
Methods
-------
rvs(M, n, N, odds, loc=0, size=1, random_state=None)
Random variates.
pmf(k, M, n, N, odds, loc=0)
Probability mass function.
logpmf(k, M, n, N, odds, loc=0)
Log of the probability mass function.
cdf(k, M, n, N, odds, loc=0)
Cumulative distribution function.
logcdf(k, M, n, N, odds, loc=0)
Log of the cumulative distribution function.
sf(k, M, n, N, odds, loc=0)
Survival function (also defined as ``1 - cdf``, but `sf` is sometimes more accurate).
logsf(k, M, n, N, odds, loc=0)
Log of the survival function.
ppf(q, M, n, N, odds, loc=0)
Percent point function (inverse of ``cdf`` --- percentiles).
isf(q, M, n, N, odds, loc=0)
Inverse survival function (inverse of ``sf``).
stats(M, n, N, odds, loc=0, moments='mv')
Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
entropy(M, n, N, odds, loc=0)
(Differential) entropy of the RV.
expect(func, args=(M, n, N, odds), loc=0, lb=None, ub=None, conditional=False)
Expected value of a function (of one argument) with respect to the distribution.
median(M, n, N, odds, loc=0)
Median of the distribution.
mean(M, n, N, odds, loc=0)
Mean of the distribution.
var(M, n, N, odds, loc=0)
Variance of the distribution.
std(M, n, N, odds, loc=0)
Standard deviation of the distribution.
interval(alpha, M, n, N, odds, loc=0)
Endpoints of the range that contains fraction alpha [0, 1] of the
distribution
See Also
--------
nchypergeom_fisher, hypergeom, nhypergeom
Notes
-----
Let mathematical symbols :math:`N`, :math:`n`, and :math:`M` correspond
with parameters `N`, `n`, and `M` (respectively) as defined above.
The probability mass function is defined as
.. math::
p(x; N, n, M) = \binom{n}{x} \binom{M - n}{N-x}
\int_0^1 \left(1-t^{\omega/D}\right)^x\left(1-t^{1/D}\right)^{N-x} dt
for
:math:`x \in [x_l, x_u]`,
:math:`M \in {\mathbb N}`,
:math:`n \in [0, M]`,
:math:`N \in [0, M]`,
:math:`\omega > 0`,
where
:math:`x_l = \max(0, N - (M - n))`,
:math:`x_u = \min(N, n)`,
.. math::
D = \omega(n - x) + ((M - n)-(N-x)),
and the binomial coefficients are defined as
.. math:: \binom{n}{k} \equiv \frac{n!}{k! (n - k)!}.
`nchypergeom_wallenius` uses the BiasedUrn package by Agner Fog with
permission for it to be distributed under SciPy's license.
The symbols used to denote the shape parameters (`N`, `n`, and `M`) are not
universally accepted; they are chosen for consistency with `hypergeom`.
Note that Wallenius' noncentral hypergeometric distribution is distinct
from Fisher's noncentral hypergeometric distribution, which models
take a handful of objects from the bin at once, finding out afterwards
that `N` objects were taken.
When the odds ratio is unity, however, both distributions reduce to the
ordinary hypergeometric distribution.
The probability mass function above is defined in the "standardized" form.
To shift distribution use the ``loc`` parameter.
Specifically, ``nchypergeom_wallenius.pmf(k, M, n, N, odds, loc)`` is identically
equivalent to ``nchypergeom_wallenius.pmf(k - loc, M, n, N, odds)``.
References
----------
.. [1] Agner Fog, "Biased Urn Theory".
https://cran.r-project.org/web/packages/BiasedUrn/vignettes/UrnTheory.pdf
.. [2] "Wallenius' noncentral hypergeometric distribution", Wikipedia,
https://en.wikipedia.org/wiki/Wallenius'_noncentral_hypergeometric_distribution
Examples
--------
>>> from scipy.stats import nchypergeom_wallenius
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)
Calculate the first four moments:
>>> M, n, N, odds = 140, 80, 60, 0.5
>>> mean, var, skew, kurt = nchypergeom_wallenius.stats(M, n, N, odds, moments='mvsk')
Display the probability mass function (``pmf``):
>>> x = np.arange(nchypergeom_wallenius.ppf(0.01, M, n, N, odds),
... nchypergeom_wallenius.ppf(0.99, M, n, N, odds))
>>> ax.plot(x, nchypergeom_wallenius.pmf(x, M, n, N, odds), 'bo', ms=8, label='nchypergeom_wallenius pmf')
>>> ax.vlines(x, 0, nchypergeom_wallenius.pmf(x, M, n, N, odds), colors='b', lw=5, alpha=0.5)
Alternatively, the distribution object can be called (as a function)
to fix the shape and location. This returns a "frozen" RV object holding
the given parameters fixed.
Freeze the distribution and display the frozen ``pmf``:
>>> rv = nchypergeom_wallenius(M, n, N, odds)
>>> ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1,
... label='frozen pmf')
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()
Check accuracy of ``cdf`` and ``ppf``:
>>> prob = nchypergeom_wallenius.cdf(x, M, n, N, odds)
>>> np.allclose(x, nchypergeom_wallenius.ppf(prob, M, n, N, odds))
True
Generate random numbers:
>>> r = nchypergeom_wallenius.rvs(M, n, N, odds, size=1000)
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