def qf(p,df1,df2,ncp=0):
"""
Calculates the quantile function of the F-distribution
"""
from scipy.stats import f,ncf
if ncp==0:
result=f.ppf(q=p,dfn=df1,dfd=df2,loc=0,scale=1)
else:
result=ncf.ppf(q=p,dfn=df1,dfd=df2,nc=ncp,loc=0,scale=1)
return result
from scipy.stats import ncf import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) # Calculate a few first moments: dfn, dfd, nc = 27, 27, 0.416 mean, var, skew, kurt = ncf.stats(dfn, dfd, nc, moments='mvsk') # Display the probability density function (``pdf``): x = np.linspace(ncf.ppf(0.01, dfn, dfd, nc), ncf.ppf(0.99, dfn, dfd, nc), 100) ax.plot(x, ncf.pdf(x, dfn, dfd, nc), 'r-', lw=5, alpha=0.6, label='ncf 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 = ncf(dfn, dfd, nc) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') # Check accuracy of ``cdf`` and ``ppf``: vals = ncf.ppf([0.001, 0.5, 0.999], dfn, dfd, nc) np.allclose([0.001, 0.5, 0.999], ncf.cdf(vals, dfn, dfd, nc)) # True # Generate random numbers: