def test_squared_normal_chi2(): #'\nsquare of standard normal random variable is chisquare with dof=1 distributed' cdftr = squarenormalg.cdf(xx,loc=l, scale=s) sfctr = 1-squarenormalg.sf(xx,loc=l, scale=s) #sf complement cdfst = stats.chi2.cdf(xx,1) assert_almost_equal(cdfst, cdftr, 14) assert_almost_equal(cdfst, sfctr, 14)
def test_squared_normal_chi2(): #'\nsquare of standard normal random variable is chisquare with dof=1 distributed' cdftr = squarenormalg.cdf(xx, loc=l, scale=s) sfctr = 1 - squarenormalg.sf(xx, loc=l, scale=s) #sf complement cdfst = stats.chi2.cdf(xx, 1) assert_almost_equal(cdfst, cdftr, 14) assert_almost_equal(cdfst, sfctr, 14)
if __name__ == '__main__': #Examples for Transf2_gen, u- or hump shaped transformation #copied from transformtwo.py l,s = 0.0, 1.0 ppfq = [0.1, 0.5, 0.9] xx = [0.95, 1.0, 1.1] nxx = [-0.95, -1.0, -1.1] print #print invnormalg.__doc__ print '\nsquare of standard normal random variable is chisquare with dof=1 distributed' print 'sqnorm cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.cdf(xx,loc=l, scale=s) print 'sqnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-squarenormalg.sf(xx,loc=l, scale=s) print 'chi2 cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1) print 'sqnorm pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.pdf(xx,loc=l, scale=s) print 'chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1) print 'sqnorm ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.ppf(ppfq,loc=l, scale=s) print 'chi2 ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.ppf(ppfq,1) print 'sqnorm cdf with loc scale', squarenormalg.cdf(xx,loc=-10, scale=20) print 'chi2 cdf with loc scale', stats.chi2.cdf(xx,1,loc=-10, scale=20) # print 'cdf for [0.5]:', squarenormalg.cdf(0.5,loc=l, scale=s) # print 'chi square distribution' # print 'chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1) # print 'cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1) print '\nabsolute value of standard normal random variable is foldnorm(0) and ' print 'halfnorm distributed:' print 'absnorm cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), absnormalg.cdf(xx,loc=l, scale=s)
if __name__ == '__main__': #Examples for Transf2_gen, u- or hump shaped transformation #copied from transformtwo.py l, s = 0.0, 1.0 ppfq = [0.1, 0.5, 0.9] xx = [0.95, 1.0, 1.1] nxx = [-0.95, -1.0, -1.1] print #print invnormalg.__doc__ print '\nsquare of standard normal random variable is chisquare with dof=1 distributed' print 'sqnorm cdf for (%3.2f, %3.2f, %3.2f):' % tuple( xx), squarenormalg.cdf(xx, loc=l, scale=s) print 'sqnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple( xx), 1 - squarenormalg.sf(xx, loc=l, scale=s) print 'chi2 cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf( xx, 1) print 'sqnorm pdf for (%3.2f, %3.2f, %3.2f):' % tuple( xx), squarenormalg.pdf(xx, loc=l, scale=s) print 'chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf( xx, 1) print 'sqnorm ppf for (%3.2f, %3.2f, %3.2f):' % tuple( xx), squarenormalg.ppf(ppfq, loc=l, scale=s) print 'chi2 ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.ppf( ppfq, 1) print 'sqnorm cdf with loc scale', squarenormalg.cdf(xx, loc=-10, scale=20) print 'chi2 cdf with loc scale', stats.chi2.cdf(xx, 1,