def calcFDistribution(v1, v2, x):
y = fdtr(v1, v2, x)
N = y.size
z = np.zeros(N)
z[1:] = y[:-1]
y -= z
return y
def cdf(self, x):
"""
Computes the cumulative distribution function of the
distribution at the point(s) x. The cdf is defined as follows:
F(x|d1, d2) = I_{(d1 * x) / (d1 * x + d2)} (d1 / 2, d2 / 2)
where I_stuff is the regularized incomplete beta function.
Parameters
----------
x: array, dtype=float, shape=(m x n)
The value(s) at which the user would like the cdf evaluated.
If an array is passed in, the cdf is evaluated at every point
in the array and an array of the same size is returned.
Returns
-------
cdf: array, dtype=float, shape=(m x n)
The cdf at each point in x.
"""
cdf = fdtr(self.d1, self.d2, x)
return cdf
def cdf(self, x):
"""
Computes the cumulative distribution function of the
distribution at the point(s) x. The cdf is defined as follows:
F(x|d1, d2) = I_{(d1 * x) / (d1 * x + d2)} (d1 / 2, d2 / 2)
where I_stuff is the regularized incomplete beta function.
Parameters
----------
x: array, dtype=float, shape=(m x n)
The value(s) at which the user would like the cdf evaluated.
If an array is passed in, the cdf is evaluated at every point
in the array and an array of the same size is returned.
Returns
-------
cdf: array, dtype=float, shape=(m x n)
The cdf at each point in x.
"""
cdf = fdtr(self.d1, self.d2, x)
return cdf
def fn(nu2):
x = k2**2 * nu2 / (nu1 * (nu2 + 1))
# return pf(x,nu1,nu2) - p
return special.fdtr(nu1, nu2, x) - p
def _cdff(self, x, dfn, dfd):
return special.fdtr(dfn, dfd, x)
def _cdff(x, dfn, dfd):
return special.fdtr(dfn, dfd, x) # pylint: disable=no-member
def _cdff(self, x, dfn, dfd):
return special.fdtr(dfn, dfd, x)
