mars.tensor.random.noncentral_f(dfnum, dfden, nonc, size=None, chunk_size=None, gpu=None, dtype=None)[source]#

Draw samples from the noncentral F distribution.

Samples are drawn from an F distribution with specified parameters, dfnum (degrees of freedom in numerator) and dfden (degrees of freedom in denominator), where both parameters > 1. nonc is the non-centrality parameter.

  • dfnum (float or array_like of floats) – Numerator degrees of freedom, should be > 0.

  • dfden (float or array_like of floats) – Denominator degrees of freedom, should be > 0.

  • nonc (float or array_like of floats) – Non-centrality parameter, the sum of the squares of the numerator means, should be >= 0.

  • size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if dfnum, dfden, and nonc are all scalars. Otherwise, np.broadcast(dfnum, dfden, nonc).size samples are drawn.

  • chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension

  • gpu (bool, optional) – Allocate the tensor on GPU if True, False as default

  • dtype (data-type, optional) – Data-type of the returned tensor.


out – Drawn samples from the parameterized noncentral Fisher distribution.

Return type

Tensor or scalar


When calculating the power of an experiment (power = probability of rejecting the null hypothesis when a specific alternative is true) the non-central F statistic becomes important. When the null hypothesis is true, the F statistic follows a central F distribution. When the null hypothesis is not true, then it follows a non-central F statistic.



Weisstein, Eric W. “Noncentral F-Distribution.” From MathWorld–A Wolfram Web Resource.


Wikipedia, “Noncentral F-distribution”,


In a study, testing for a specific alternative to the null hypothesis requires use of the Noncentral F distribution. We need to calculate the area in the tail of the distribution that exceeds the value of the F distribution for the null hypothesis. We’ll plot the two probability distributions for comparison.

>>> import mars.tensor as mt
>>> import matplotlib.pyplot as plt
>>> dfnum = 3 # between group deg of freedom
>>> dfden = 20 # within groups degrees of freedom
>>> nonc = 3.0
>>> nc_vals = mt.random.noncentral_f(dfnum, dfden, nonc, 1000000)
>>> NF = np.histogram(nc_vals.execute(), bins=50, normed=True)  # TODO(jisheng): implement mt.histogram
>>> c_vals = mt.random.f(dfnum, dfden, 1000000)
>>> F = np.histogram(c_vals.execute(), bins=50, normed=True)
>>> plt.plot(F[1][1:], F[0])
>>> plt.plot(NF[1][1:], NF[0])