# scipy.stats.ncx2¶

scipy.stats.ncx2

A non-central 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, nc : 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 = ncx2(df, nc, loc=0, scale=1) : Frozen RV object with the same methods but holding the given shape, location, and scale fixed.

Notes

Non-central chi-squared distribution

ncx2.pdf(x,df,nc) = exp(-(nc+df)/2)*1/2*(x/nc)**((df-2)/4)
• I[(df-2)/2](sqrt(nc*x))

for x > 0.

Examples

```>>> import matplotlib.pyplot as plt
>>> numargs = ncx2.numargs
>>> [ df, nc ] = [0.9,] * numargs
>>> rv = ncx2(df, nc)
```

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 = ncx2.cdf(x, df, nc)
>>> h = plt.semilogy(np.abs(x - ncx2.ppf(prb, df, nc)) + 1e-20)
```

Random number generation

```>>> R = ncx2.rvs(df, nc, size=100)
```

Methods

 rvs(df, nc, loc=0, scale=1, size=1) Random variates. pdf(x, df, nc, loc=0, scale=1) Probability density function. cdf(x, df, nc, loc=0, scale=1) Cumulative density function. sf(x, df, nc, loc=0, scale=1) Survival function (1-cdf — sometimes more accurate). ppf(q, df, nc, loc=0, scale=1) Percent point function (inverse of cdf — percentiles). isf(q, df, nc, loc=0, scale=1) Inverse survival function (inverse of sf). stats(df, nc, loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(df, nc, loc=0, scale=1) (Differential) entropy of the RV. fit(data, df, nc, loc=0, scale=1) Parameter estimates for generic data.

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scipy.stats.ncf