scipy.stats.pearson3

scipy.stats.pearson3 = <scipy.stats._continuous_distns.pearson3_gen object>

A pearson type III continuous random variable.

As an instance of the rv_continuous class, pearson3 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.

Notes

The probability density function for pearson3 is:

\[f(x, skew) = \frac{|\beta|}{\gamma(\alpha)} (\beta (x - \zeta))^{alpha - 1} \exp(-\beta (x - \zeta))\]

where:

\[\beta = \frac{2}{skew stddev} \alpha = (stddev \beta)^2 \zeta = loc - \frac{\alpha}{\beta}\]

pearson3 takes skew as a shape parameter.

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, pearson3.pdf(x, skew, loc, scale) is identically equivalent to pearson3.pdf(y, skew) / scale with y = (x - loc) / scale.

References

R.W. Vogel and D.E. McMartin, “Probability Plot Goodness-of-Fit and Skewness Estimation Procedures for the Pearson Type 3 Distribution”, Water Resources Research, Vol.27, 3149-3158 (1991).

L.R. Salvosa, “Tables of Pearson’s Type III Function”, Ann. Math. Statist., Vol.1, 191-198 (1930).

“Using Modern Computing Tools to Fit the Pearson Type III Distribution to Aviation Loads Data”, Office of Aviation Research (2003).

Examples

>>> from scipy.stats import pearson3
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)

Calculate a few first moments:

>>> skew = 0.1
>>> mean, var, skew, kurt = pearson3.stats(skew, moments='mvsk')

Display the probability density function (pdf):

>>> x = np.linspace(pearson3.ppf(0.01, skew),
...                 pearson3.ppf(0.99, skew), 100)
>>> ax.plot(x, pearson3.pdf(x, skew),
...        'r-', lw=5, alpha=0.6, label='pearson3 pdf')

Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

Freeze the distribution and display the frozen pdf:

>>> rv = pearson3(skew)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of cdf and ppf:

>>> vals = pearson3.ppf([0.001, 0.5, 0.999], skew)
>>> np.allclose([0.001, 0.5, 0.999], pearson3.cdf(vals, skew))
True

Generate random numbers:

>>> r = pearson3.rvs(skew, size=1000)

And compare the histogram:

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

Methods

rvs(skew, loc=0, scale=1, size=1, random_state=None)	Random variates.
pdf(x, skew, loc=0, scale=1)	Probability density function.
logpdf(x, skew, loc=0, scale=1)	Log of the probability density function.
cdf(x, skew, loc=0, scale=1)	Cumulative distribution function.
logcdf(x, skew, loc=0, scale=1)	Log of the cumulative distribution function.
sf(x, skew, loc=0, scale=1)	Survival function (also defined as 1 - cdf, but sf is sometimes more accurate).
logsf(x, skew, loc=0, scale=1)	Log of the survival function.
ppf(q, skew, loc=0, scale=1)	Percent point function (inverse of cdf — percentiles).
isf(q, skew, loc=0, scale=1)	Inverse survival function (inverse of sf).
moment(n, skew, loc=0, scale=1)	Non-central moment of order n
stats(skew, loc=0, scale=1, moments='mv')	Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(skew, loc=0, scale=1)	(Differential) entropy of the RV.
fit(data, skew, loc=0, scale=1)	Parameter estimates for generic data.
expect(func, args=(skew,), 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(skew, loc=0, scale=1)	Median of the distribution.
mean(skew, loc=0, scale=1)	Mean of the distribution.
var(skew, loc=0, scale=1)	Variance of the distribution.
std(skew, loc=0, scale=1)	Standard deviation of the distribution.
interval(alpha, skew, loc=0, scale=1)	Endpoints of the range that contains alpha percent of the distribution