# macroeco.models.geom¶

macroeco.models.geom = <macroeco.models._distributions.geom_gen object at 0x1082d3f50>

A geometric discrete random variable.

This implementation of the geometric distribution differs from that in scipy.stats, as the distribution here has support from 0 to inf.

$P(x) = (1-p)^{x} p$

for x >= 0. The loc parameter is not used.

Parameters: x : array_like quantiles q : array_like lower or upper tail probability p : array_like shape parameters loc : array_like, optional location parameter (default=0) 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 and : location parameters returning a “frozen” discrete RV object: : rv = geom(p, loc=0) : Frozen RV object with the same methods but holding the given shape and location fixed. mu : float distribution mean

Examples

>>> import macroeco.models as md

>>> # Get the geom_parameters from a mean
>>> mu = 20
>>> p = md.geom.translate_args(mu)
0.047619047619047616

>>> # Get the pmf
>>> md.geom.pmf(np.arange(0, 5), p)
array([ 0.04761905,  0.04535147,  0.04319188,  0.04113512,  0.03917631])

>>> # Generate a rank abundance distribution
>>> rad = md.geom.rank(20, p)

>>> # Fit the geom to data