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src/c/l/CladeCompare-0.2/cladecomparelib/ancestrallrt.py   CladeCompare(Download)
        bg_char, L0 = max(col0.items(), key=value_of)
        LLR = 2 * (math.log(L1) - math.log(L0))
        pvalue = stats.chisqprob(LLR, df)
        # XXX DBG
        logging.info("%s/%s %d : H0=%g, H1=%g, LLR=%g, p=%g"

src/e/e/eebprogramming-HEAD/lec10review/10rr.py   eebprogramming(Download)
LR = 2 * (lf.getLogLikelihood() - null_lnL)
df = lf.getNumFreeParams() - null_nfp
P = stats.chisqprob(LR, df)
print "Likelihood ratio statistic = ", LR
print "degrees-of-freedom = ", df

src/q/i/qiime-1.8.0/qiime/pycogent_backports/test.py   qiime(Download)
from cogent.maths.stats.special import (lgam, log_one_minus, one_minus_exp,
    MACHEP)
from cogent.maths.stats import chisqprob
from cogent.maths.stats.ks import psmirnov2x, pkstwo
from cogent.maths.stats.special import Gamma
    else:
        # give chisqprob the kw statistic, degrees of freedom = (num groups - 1)
        p_value = chisqprob(H/D, num_groups-1)
        return H/D, p_value
## End functions for kruskal_wallis test
    # calculate homogeneity
    x_2 = ((ns-3)*(zs-z_bar)**2).sum()
    h_val = chisqprob(x_2, len(ns)-1)
    return rho, h_val
 

src/q/i/qiime-1.8.0/tests/test_pycogent_backports/test_test.py   qiime(Download)
from qiime.pycogent_backports.test import (cscore)
# cogent imports
from cogent.maths.stats import chisqprob
from cogent.maths.stats.distribution import zprob, tprob
 
        X2 = 15.26352
        pop_r = .547825
        hval = chisqprob(X2, len(ns)-1)
        obs_p_rho, obs_hval = fisher_population_correlation(rs, ns)
        self.assertFloatEqual(obs_p_rho, pop_r)

src/q/i/qiime-HEAD/qiime/pycogent_backports/test.py   qiime(Download)
                                             ndtri)
from cogent.maths.stats.special import log_one_minus, one_minus_exp, MACHEP
from cogent.maths.stats import chisqprob
from cogent.maths.stats.ks import psmirnov2x, pkstwo
from cogent.maths.stats.special import Gamma
        # give chisqprob the kw statistic, degrees of freedom = (num groups -
        # 1)
        p_value = chisqprob(H / D, num_groups - 1)
        return H / D, p_value
# End functions for kruskal_wallis test
    # calculate homogeneity
    x_2 = ((ns - 3) * (zs - z_bar) ** 2).sum()
    h_val = chisqprob(x_2, len(ns) - 1)
    return rho, h_val
 

src/q/i/qiime-HEAD/tests/test_pycogent_backports/test_test.py   qiime(Download)
from qiime.pycogent_backports.test import (cscore)
# cogent imports
from cogent.maths.stats import chisqprob
from cogent.maths.stats.distribution import zprob, tprob
 
        X2 = 15.26352
        pop_r = .547825
        hval = chisqprob(X2, len(ns) - 1)
        obs_p_rho, obs_hval = fisher_population_correlation(rs, ns)
        self.assertFloatEqual(obs_p_rho, pop_r)