scipy.stats.chi2

scipy.stats.chi2()

A chi-squared 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

df : 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 : string, 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’)

Methods:

chi2.rvs(df,loc=0,scale=1,size=1) :

• random variates

chi2.pdf(x,df,loc=0,scale=1) :

• probability density function

chi2.cdf(x,df,loc=0,scale=1) :

• cumulative density function

chi2.sf(x,df,loc=0,scale=1) :

• survival function (1-cdf — sometimes more accurate)

chi2.ppf(q,df,loc=0,scale=1) :

• percent point function (inverse of cdf — percentiles)

chi2.isf(q,df,loc=0,scale=1) :

• inverse survival function (inverse of sf)

chi2.stats(df,loc=0,scale=1,moments=’mv’) :

• mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’)

chi2.entropy(df,loc=0,scale=1) :

• (differential) entropy of the RV.

chi2.fit(data,df,loc=0,scale=1) :

• Parameter estimates for chi2 data

Alternatively, the object may be called (as a function) to fix the shape, :

location, and scale parameters returning a “frozen” continuous RV object: :

rv = chi2(df,loc=0,scale=1) :

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

Examples

>>> import matplotlib.pyplot as plt
>>> numargs = chi2.numargs
>>> [ df ] = [0.9,]*numargs
>>> rv = chi2(df)

Display frozen pdf

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

Check accuracy of cdf and ppf

>>> prb = chi2.cdf(x,df)
>>> h=plt.semilogy(np.abs(x-chi2.ppf(prb,c))+1e-20)

Random number generation

>>> R = chi2.rvs(df,size=100)

Chi-squared distribution

chi2.pdf(x,df) = 1/(2*gamma(df/2)) * (x/2)**(df/2-1) * exp(-x/2)