python.test_statistics Namespace Reference

Functions
def	ks_2samp (data1, data2, binned=False)
def	chi2_2samp (data1, data2, normed=True, binned=True)
def	BDM_2samp (data1, data2, normed=True, binned=True)
def	CVM_2samp (data1, data2, normed=True, binned=False)
def	_anderson_ksamp_midrank_binned (data, Z, Zstar, k, n, N)
def	_anderson_ksamp_midrank (samples, Z, Zstar, k, n, N)
def	_anderson_ksamp_right (samples, Z, Zstar, k, n, N)
def	anderson_ksamp (data1, data2, binned=False, midrank=True)
def	_log_fac (m, n)
def	_log_binomial (n, k)
def	_loglikelihood (data1, data2, N_data1, N_data2, ratio, H="H0")
def	_zloglikelihood (data1, data2, N_data1, N_data2, H="H0")
def	likelihoodratio_ksamp (data1, data2, normed=True, binned=True)
def	likelihoodvalue_ksamp (data1, data2, normed=True, binned=True)

Variables
bool	DEBUG = False
list	statistic_seq = []
	Ks_2sampResult = namedtuple('Ks_2sampResult', ('statistic', 'pvalue','ndf'))
	Chi2_2sampResult = namedtuple('Chi2_2sampResult', ('statistic', 'pvalue','ndf'))
	BDM_2sampResult = namedtuple('BDM_2sampResult', ('statistic', 'pvalue', 'ndf'))
	CVM_2sampResult = namedtuple('CVM_2sampResult', ('statistic', 'pvalue', 'ndf'))
	Anderson_ksampResult = namedtuple('Anderson_ksampResult', ('statistic', 'pvalue', 'ndf'))
	LikelihoodRatio_ksampResult = namedtuple('LikelihoodRatio_ksampResult', ('statistic', 'pvalue', 'ndf'))
	LikelihoodValue_ksampResult = namedtuple('LikelihoodValue_ksampResult', ('statistic', 'pvalue', 'ndf'))

Function Documentation

_anderson_ksamp_midrank()

def python.test_statistics._anderson_ksamp_midrank (   samples,   Z,   Zstar,   k,   n,   N  )

private

Compute A2akN equation 7 of Scholz and Stephens.

Parameters
----------
samples : sequence of 1-D array_like
    Array of sample arrays.
Z : array_like
    Sorted array of all observations.
Zstar : array_like
    Sorted array of unique observations.
k : int
    Number of samples.
n : array_like
    Number of observations in each sample.
N : int
    Total number of observations.

Returns
-------
A2aKN : float
    The A2aKN statistics of Scholz and Stephens 1987.

Definition at line 309 of file test_statistics.py.

def _anderson_ksamp_midrank(samples, Z, Zstar, k, n, N):
    """
    Compute A2akN equation 7 of Scholz and Stephens.
 
    Parameters
    ----------
    samples : sequence of 1-D array_like
        Array of sample arrays.
    Z : array_like
        Sorted array of all observations.
    Zstar : array_like
        Sorted array of unique observations.
    k : int
        Number of samples.
    n : array_like
        Number of observations in each sample.
    N : int
        Total number of observations.
 
    Returns
    -------
    A2aKN : float
        The A2aKN statistics of Scholz and Stephens 1987.
    """
    
    if DEBUG:
        global statistic_seq
        statistic_seq = []
    
    A2akN = 0.
    Z_ssorted_left = Z.searchsorted(Zstar, 'left')
    if N == Zstar.size:
        lj = 1.
    else:
        lj = Z.searchsorted(Zstar, 'right') - Z_ssorted_left
    Bj = Z_ssorted_left + lj / 2.
    for i in np.arange(0, k):
        s = np.sort(samples[i])
        s_ssorted_right = s.searchsorted(Zstar, side='right')
        Mij = s_ssorted_right.astype(float)
        fij = s_ssorted_right - s.searchsorted(Zstar, 'left')
        Mij -= fij / 2.
        inner = lj / float(N) * (N*Mij - Bj*n[i])**2 / (Bj*(N - Bj) - N*lj/4.)
        A2akN += inner.sum() / n[i]
        
        if DEBUG:
            statistic_seq.append(A2akN*(N-1.)/N)
        
    A2akN *= (N - 1.) / N
    return A2akN
 
 

_anderson_ksamp_midrank_binned()

def python.test_statistics._anderson_ksamp_midrank_binned (   data,   Z,   Zstar,   k,   n,   N  )

private

Definition at line 288 of file test_statistics.py.

def _anderson_ksamp_midrank_binned(data, Z, Zstar, k, n, N):
    
    if DEBUG:
        global statistic_seq
        statistic_seq = []
    
    A2akN = 0.
    lj = Z
    Bj = Z.cumsum() - lj/2
    for i in np.arange(0, k):
        d = data[i]
        Mij = d.cumsum() - d/2
        inner = lj / float(N) * (N*Mij - Bj*n[i])**2 / (Bj*(N - Bj) - N*lj/4.)
        A2akN += np.nan_to_num(inner).sum() / n[i]
        
        if DEBUG:
            statistic_seq.append(A2akN*(N-1.)/N)
        
    A2akN *= (N - 1.) / N
    return A2akN
 

_anderson_ksamp_right()

def python.test_statistics._anderson_ksamp_right (   samples,   Z,   Zstar,   k,   n,   N  )

private

Compute A2akN equation 6 of Scholz & Stephens.

Parameters
----------
samples : sequence of 1-D array_like
    Array of sample arrays.
Z : array_like
    Sorted array of all observations.
Zstar : array_like
    Sorted array of unique observations.
k : int
    Number of samples.
n : array_like
    Number of observations in each sample.
N : int
    Total number of observations.

Returns
-------
A2KN : float
    The A2KN statistics of Scholz and Stephens 1987.

Definition at line 361 of file test_statistics.py.

def _anderson_ksamp_right(samples, Z, Zstar, k, n, N):
    """
    Compute A2akN equation 6 of Scholz & Stephens.
 
    Parameters
    ----------
    samples : sequence of 1-D array_like
        Array of sample arrays.
    Z : array_like
        Sorted array of all observations.
    Zstar : array_like
        Sorted array of unique observations.
    k : int
        Number of samples.
    n : array_like
        Number of observations in each sample.
    N : int
        Total number of observations.
 
    Returns
    -------
    A2KN : float
        The A2KN statistics of Scholz and Stephens 1987.
    """
 
    A2kN = 0.
    lj = Z.searchsorted(Zstar[:-1], 'right') - Z.searchsorted(Zstar[:-1],  'left')
    Bj = lj.cumsum()
    for i in range(0, k):
        s = np.sort(samples[i])
        Mij = s.searchsorted(Zstar[:-1], side='right')
        inner = lj / float(N) * (N * Mij - Bj * n[i])**2 / (Bj * (N - Bj))
        A2kN += inner.sum() / n[i]
    return A2kN
 
 

_log_binomial()

def python.test_statistics._log_binomial (   n,   k  )

private

Definition at line 542 of file test_statistics.py.

def _log_binomial(n,k): 
    if k > (n-k):
        return (_log_fac(n-k+1,n)-_log_fac(2,k))
    else:
        return (_log_fac(k+1,n)-_log_fac(2,n-k))
 

_log_fac()

def python.test_statistics._log_fac (   m,   n  )

private

Definition at line 539 of file test_statistics.py.

def _log_fac(m,n):
    return np.log(np.arange(m,n+1)).sum()
@np.vectorize

_loglikelihood()

def python.test_statistics._loglikelihood (   data1,   data2,   N_data1,   N_data2,   ratio,   H = "H0"  )

private

Definition at line 548 of file test_statistics.py.

def _loglikelihood(data1, data2, N_data1, N_data2, ratio, H = "H0"):
    t = data1 + data2
    logC = 0.
    if not ratio:
        logC = float(sum(log(binomial(t[i], data2[i])) for i in range(t.shape[0])))
    if H=="H1":
        a = data2/data1
    if H=="H0":
        a = N_data2 / N_data1
    
    if DEBUG:
        global statistic_seq
        statistic_seq = -2 * (logC - scipy.special.xlogy(t, 1+a) + scipy.special.xlogy(data2, a))
    
    return -2 * (logC - scipy.special.xlogy(t, 1+a).sum() + scipy.special.xlogy(data2, a).sum())
 

_zloglikelihood()

def python.test_statistics._zloglikelihood (   data1,   data2,   N_data1,   N_data2,   H = "H0"  )

private

Definition at line 564 of file test_statistics.py.

def _zloglikelihood(data1, data2, N_data1, N_data2, H = "H0"):
    z_not_in_both = (data1 == 0) ^ (data2 == 0)
    data1 = data1[z_not_in_both]
    data2 = data2[z_not_in_both]
    t = data1 + data2
    if H == "H0":
        p = N_data1 / (N_data1 + N_data2)
        
        if DEBUG:
            global statistic_seq
            statistic_seq = np.concatenate([statistic_seq, -2 * scipy.special.xlogy(t[data2==0], p), scipy.special.xlogy(t[data1 == 0], 1-p)])
        
        return -2 * (scipy.special.xlogy(t[data2==0], p).sum() + scipy.special.xlogy(t[data1 == 0], 1-p).sum())
    if H == "H1":
        return 0.
 

anderson_ksamp()

def python.test_statistics.anderson_ksamp	(		data1,
			data2,
			binned = False,
			midrank = True
	)

The Anderson-Darling test for 2 samples.

It tests the null hypothesis
that the 2 samples are drawn from the same population without
having to specify the distribution function of that population.
The critical values depend on the number of samples.

Parameters
----------
samples : sequence of 1-D array_like
    Array of sample data in arrays.

Returns
-------
statistic : float
    AD statistic
pvalue : float
    two-tailed p-value
ndf : NaN
    「Degrees of freedom」: the degrees of freedom for the p-value.
    Always NaN in this case.

Raises
------
ValueError
    If less than 2 samples are provided, a sample is empty, or no
    distinct observations are in the samples.

See Also
--------
ks_2samp : 2 sample Kolmogorov-Smirnov test

Notes
-----
This code is modified from scipy.stats.anderson and extended with
supporting on frequency data. See [1]_ for further information.

[2]_ Defines three versions of the k-sample Anderson-Darling test:
one for continuous distributions and two for discrete
distributions, in which ties between samples may occur. The
default of this routine is to compute the version based on the
midrank empirical distribution function. This test is applicable
to continuous and discrete data. If midrank is set to False, the
right side empirical distribution is used for a test for discrete
data. According to [1]_, the two discrete test statistics differ
only slightly if a few collisions due to round-off errors occur in
the test not adjusted for ties between samples.

References
----------
.. [1] scipy.stats.anderson — SciPy Reference Guide
       http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.anderson.html
.. [2] Scholz, F. W and Stephens, M. A. (1987), K-Sample
       Anderson-Darling Tests, Journal of the American Statistical
       Association, Vol. 82, pp. 918-924.

Examples
--------
>>> from scipy import stats
>>> np.random.seed(314159)

The null hypothesis that the two random samples come from the same
distribution can be rejected at the 5% level because the returned
test value is greater than the critical value for 5% (1.961) but
not at the 2.5% level. The interpolation gives an approximate
significance level of 3.1%:

>>> stats.anderson_ksamp([np.random.normal(size=50),
... np.random.normal(loc=0.5, size=30)])
(2.4615796189876105,
  array([ 0.325,  1.226,  1.961,  2.718,  3.752]),
  0.03134990135800783)


The null hypothesis cannot be rejected for three samples from an
identical distribution. The approximate p-value (87%) has to be
computed by extrapolation and may not be very accurate:

>>> stats.anderson_ksamp([np.random.normal(size=50),
... np.random.normal(size=30), np.random.normal(size=20)])
(-0.73091722665244196,
  array([ 0.44925884,  1.3052767 ,  1.9434184 ,  2.57696569,  3.41634856]),
  0.8789283903979661)

Definition at line 398 of file test_statistics.py.

def anderson_ksamp(data1, data2, binned=False, midrank=True):
    """The Anderson-Darling test for 2 samples.
 
    It tests the null hypothesis
    that the 2 samples are drawn from the same population without
    having to specify the distribution function of that population.
    The critical values depend on the number of samples.
 
    Parameters
    ----------
    samples : sequence of 1-D array_like
        Array of sample data in arrays.
    
    Returns
    -------
    statistic : float
        AD statistic
    pvalue : float
        two-tailed p-value
    ndf : NaN
        「Degrees of freedom」: the degrees of freedom for the p-value.
        Always NaN in this case.
 
    Raises
    ------
    ValueError
        If less than 2 samples are provided, a sample is empty, or no
        distinct observations are in the samples.
 
    See Also
    --------
    ks_2samp : 2 sample Kolmogorov-Smirnov test
 
    Notes
    -----
    This code is modified from scipy.stats.anderson and extended with
    supporting on frequency data. See [1]_ for further information.
 
    [2]_ Defines three versions of the k-sample Anderson-Darling test:
    one for continuous distributions and two for discrete
    distributions, in which ties between samples may occur. The
    default of this routine is to compute the version based on the
    midrank empirical distribution function. This test is applicable
    to continuous and discrete data. If midrank is set to False, the
    right side empirical distribution is used for a test for discrete
    data. According to [1]_, the two discrete test statistics differ
    only slightly if a few collisions due to round-off errors occur in
    the test not adjusted for ties between samples.
 
    References
    ----------
    .. [1] scipy.stats.anderson — SciPy Reference Guide
           http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.anderson.html
    .. [2] Scholz, F. W and Stephens, M. A. (1987), K-Sample
           Anderson-Darling Tests, Journal of the American Statistical
           Association, Vol. 82, pp. 918-924.
 
    Examples
    --------
    >>> from scipy import stats
    >>> np.random.seed(314159)
 
    The null hypothesis that the two random samples come from the same
    distribution can be rejected at the 5% level because the returned
    test value is greater than the critical value for 5% (1.961) but
    not at the 2.5% level. The interpolation gives an approximate
    significance level of 3.1%:
 
    >>> stats.anderson_ksamp([np.random.normal(size=50),
    ... np.random.normal(loc=0.5, size=30)])
    (2.4615796189876105,
      array([ 0.325,  1.226,  1.961,  2.718,  3.752]),
      0.03134990135800783)
 
 
    The null hypothesis cannot be rejected for three samples from an
    identical distribution. The approximate p-value (87%) has to be
    computed by extrapolation and may not be very accurate:
 
    >>> stats.anderson_ksamp([np.random.normal(size=50),
    ... np.random.normal(size=30), np.random.normal(size=20)])
    (-0.73091722665244196,
      array([ 0.44925884,  1.3052767 ,  1.9434184 ,  2.57696569,  3.41634856]),
      0.8789283903979661)
 
    """
    samples = [data1, data2]
    k = len(samples)
    if (k < 2):
        raise ValueError("anderson_ksamp needs at least two samples")
 
    samples = list(map(np.asarray, samples))
    if binned:
        Z = Zstar = sum(samples)
        n = np.array([sample.sum() for sample in samples])
        N = n.sum()
        A2kN = _anderson_ksamp_midrank_binned(samples, Z, Zstar, k, n, N)
    else:
        Z = np.sort(np.hstack(samples))
        N = Z.size
        Zstar = np.unique(Z)
        if Zstar.size < 2:
            raise ValueError("anderson_ksamp needs more than one distinct "
                             "observation")
 
        n = np.array([sample.size for sample in samples])
        if any(n == 0):
            raise ValueError("anderson_ksamp encountered sample without "
                             "observations")
 
        if midrank:
            A2kN = _anderson_ksamp_midrank(samples, Z, Zstar, k, n, N)
        else:
            A2kN = _anderson_ksamp_right(samples, Z, Zstar, k, n, N)
 
    H = (1. / n).sum()
    hs_cs = (1. / np.arange(N - 1, 1, -1)).cumsum()
    h = hs_cs[-1] + 1
    g = (hs_cs / np.arange(2, N)).sum()
 
    a = (4*g - 6) * (k - 1) + (10 - 6*g)*H
    b = (2*g - 4)*k**2 + 8*h*k + (2*g - 14*h - 4)*H - 8*h + 4*g - 6
    c = (6*h + 2*g - 2)*k**2 + (4*h - 4*g + 6)*k + (2*h - 6)*H + 4*h
    d = (2*h + 6)*k**2 - 4*h*k
    sigmasq = (a*N**3 + b*N**2 + c*N + d) / ((N - 1.) * (N - 2.) * (N - 3.))
    m = k - 1
    A2 = (A2kN - m) / np.sqrt(sigmasq)
 
    # The b_i values are the interpolation coefficients from Table 2
    # of Scholz and Stephens 1987
    b0 = np.array([0.675, 1.281, 1.645, 1.96, 2.326])
    b1 = np.array([-0.245, 0.25, 0.678, 1.149, 1.822])
    b2 = np.array([-0.105, -0.305, -0.362, -0.391, -0.396])
    critical = b0 + b1 / np.sqrt(m) + b2 / m
    pf = np.polyfit(critical, np.log(np.array([0.25, 0.1, 0.05, 0.025, 0.01])), 2)
    if A2 < critical.min() or A2 > critical.max():
        warnings.warn("approximate p-value will be computed by extrapolation")  # noqa: B028 (no stacklevel needed)
 
    p = np.exp(np.polyval(pf, A2))
    return Anderson_ksampResult(A2, p, float('nan'))
 

BDM_2samp()

def python.test_statistics.BDM_2samp	(		data1,
			data2,
			normed = True,
			binned = True
	)

The Bhattacharyya test tests the null hypothesis that 2 given frequencies are drawn
from the same distribution.    

Parameters
----------
data1, data2 : sequence of 1-D ndarrays
    Input data. Observed frequencies in each category.
normed : bool, optional
    If True/False, shape/value comparison test.
binned : bool, optional
    If True/False, assume the type of input data is observed frequencies/samples.

Returns
-------
statistic : float
    The Bhattacharyya distance measure.
pvalue : float
    Corresponding p-value.
ndf : int
    「Degrees of freedom」: the degrees of freedom for the p-value. The p-value
    is computed using a chi-squared distribution with k - 1 degrees of freedom,
    where k is the number of observed frequencies without bins has zero counts 
    in both histograms.

Definition at line 196 of file test_statistics.py.

def BDM_2samp(data1, data2, normed=True, binned=True):
    """
    The Bhattacharyya test tests the null hypothesis that 2 given frequencies are drawn
    from the same distribution.    
 
    Parameters
    ----------
    data1, data2 : sequence of 1-D ndarrays
        Input data. Observed frequencies in each category.
    normed : bool, optional
        If True/False, shape/value comparison test.
    binned : bool, optional
        If True/False, assume the type of input data is observed frequencies/samples.
    
    Returns
    -------
    statistic : float
        The Bhattacharyya distance measure.
    pvalue : float
        Corresponding p-value.
    ndf : int
        「Degrees of freedom」: the degrees of freedom for the p-value. The p-value
        is computed using a chi-squared distribution with k - 1 degrees of freedom,
        where k is the number of observed frequencies without bins has zero counts 
        in both histograms.
    """
    if binned:
        filter = ~((data1 == 0.) & (data2 == 0.))
        data1 = data1[filter] 
        data2 = data2[filter] 
        ndf = data1.shape[0]
        N_data1 = data1.sum()
        N_data2 = data2.sum()
        bdm = np.sqrt(data1 * data2).sum()
        approx_chi2 = 2*N_data1+2*N_data2 - 4*bdm
        if normed:
            ndf -= 1
            bdm /= np.sqrt(N_data1 * N_data2)
    if DEBUG:
        global statistic_seq
        statistic_seq = 2*data1+2*data2 - 4*np.sqrt(data1 * data2)
    return BDM_2sampResult(bdm, scipy.stats.chi2.sf(approx_chi2, ndf), ndf)
 

chi2_2samp()

def python.test_statistics.chi2_2samp	(		data1,
			data2,
			normed = True,
			binned = True
	)

The chi square test tests the null hypothesis that 2 given frequencies are drawn
from the same distribution.

Parameters
----------
data1, data2 : sequence of 1-D ndarrays
    Input data. Observed frequencies in each category.
normed : bool, optional, default: True
    If True/False, shape/value comparison test.
binned : bool, optional, default: True
    If True/False, assume the type of input data is observed frequencies/samples.

Returns
-------
statistic : float
    The chi-squared test statistic.
pvalue : float
    Corresponding p-value.
ndf : int
    「Degrees of freedom」: the degrees of freedom for the p-value. The p-value
    is computed using a chi-squared distribution with k - 1 degrees of freedom,
    where k is the number of observed frequencies without bins has zero counts 
    in both histograms.

Note
----
This code is modified from scipy.stats.chisquare and extended with supporting on
2 sample cases and shape comparison test. 

Definition at line 142 of file test_statistics.py.

def chi2_2samp(data1, data2, normed=True, binned=True):
    """
    The chi square test tests the null hypothesis that 2 given frequencies are drawn
    from the same distribution.
    
    Parameters
    ----------
    data1, data2 : sequence of 1-D ndarrays
        Input data. Observed frequencies in each category.
    normed : bool, optional, default: True
        If True/False, shape/value comparison test.
    binned : bool, optional, default: True
        If True/False, assume the type of input data is observed frequencies/samples.
    
    Returns
    -------
    statistic : float
        The chi-squared test statistic.
    pvalue : float
        Corresponding p-value.
    ndf : int
        「Degrees of freedom」: the degrees of freedom for the p-value. The p-value
        is computed using a chi-squared distribution with k - 1 degrees of freedom,
        where k is the number of observed frequencies without bins has zero counts 
        in both histograms.
 
    Note
    ----
    This code is modified from scipy.stats.chisquare and extended with supporting on
    2 sample cases and shape comparison test. 
    """
    if binned is True:
        filter = ~((data1 == 0.) & (data2 == 0.))
        data1 = data1[filter] 
        data2 = data2[filter] 
        ndf = data1.shape[0]
        if not normed:
            chi2 = np.power(data1 - data2, 2).dot(np.power(data1 + data2, -1))
        else:
            ndf -= 1
            N_data1 = data1.sum()
            N_data2 = data2.sum()
            data1 /= N_data1
            data2 /= N_data2
            chi2 = np.power(data1 - data2, 2).dot(np.power(data1/N_data1 + data2/N_data2, -1))
    if DEBUG:
        global statistic_seq
        if not normed:
            statistic_seq = np.power(data1 - data2, 2) * (np.power(data1 + data2, -1))
        else:
            statistic_seq = np.power(data1 - data2, 2) * np.power(data1/N_data1 + data2/N_data2, -1)
    return Chi2_2sampResult(chi2, scipy.stats.chi2.sf(chi2, ndf), ndf)
 

CVM_2samp()

def python.test_statistics.CVM_2samp	(		data1,
			data2,
			normed = True,
			binned = False
	)

Computes the Cram ́er-von-Mises statistic on 2 samples/frequencies.

This is a two-sided test for the null hypothesis that 2 independent samples
are drawn from the same continuous distribution.

Parameters
----------
data1 ,data2 : sequence of 1-D ndarrays
    Input data. Can be either observed frequencies in each category or array
    of sample observations.
binned : bool, optional, default: False
    If True/False, assume the type of input data is observed frequencies/samples.

Returns
-------
statistic : float
    CVM statistic
pvalue : float
    two-tailed p-value
ndf : NaN
    「Degrees of freedom」: the degrees of freedom for the p-value. Always NaN in this case.

Definition at line 240 of file test_statistics.py.

def CVM_2samp(data1, data2, normed=True, binned=False):
    """
    Computes the Cram ́er-von-Mises statistic on 2 samples/frequencies.
 
    This is a two-sided test for the null hypothesis that 2 independent samples
    are drawn from the same continuous distribution.
 
    Parameters
    ----------
    data1 ,data2 : sequence of 1-D ndarrays
        Input data. Can be either observed frequencies in each category or array
        of sample observations.
    binned : bool, optional, default: False
        If True/False, assume the type of input data is observed frequencies/samples.
    
    Returns
    -------
    statistic : float
        CVM statistic
    pvalue : float
        two-tailed p-value
    ndf : NaN
        「Degrees of freedom」: the degrees of freedom for the p-value. Always NaN in this case.
    """
    N1 = data1.shape[0]
    N2 = data2.shape[0]
    N = N1 + N2
    if binned:
        E1 = data1.sum()
        E2 = data2.sum()
        cdf1 = np.cumsum(data1) / E1
        cdf2 = np.cumsum(data2) / E2
        weights = data1 + data2
    else:
        E1 = N1
        E2 = N2
        data_all = np.concatenate([data1, data2])
        cdf1 = np.searchsorted(data1, data_all, side='right') / (1.0*N1)
        cdf2 = np.searchsorted(data2, data_all, side='right') / (1.0*N2)
        weights = 1.
    cvm = (weights * (cdf1 - cdf2)**2).sum() * E1 * E2 / (E1 + E2)**2
    mean = 1./6 + 1./6/N
    var = (N + 1.) * (4.*N1*N2*N - 3*(N1**2+N2**2) - 2*N1*N2) / (4 * N1 * N2) /N**2/45
    if DEBUG:
        global statistic_seq
        statistic_seq = (weights * (cdf1 - cdf2)**2) * E1 * E2 / (E1 + E2)**2
    return CVM_2sampResult(cvm, 1-distr.CVM(cvm, mean, var), float("nan"))
 

ks_2samp()

def python.test_statistics.ks_2samp	(		data1,
			data2,
			binned = False
	)

Computes the Kolmogorov-Smirnov statistic on 2 samples/frequencies.

This is a two-sided test for the null hypothesis that 2 independent samples
are drawn from the same continuous distribution.

Parameters
----------
data1, data2 : sequence of 1-D ndarrays
    Input data. Can be either observed frequencies in each category or array
    of sample observations.
binned : bool, optional, default: False
    If True/False, assume the type of input data is observed frequencies/samples.

Returns
-------
statistic : float
    KS statistic
pvalue : float
    two-tailed p-value
ndf : NaN
    「Degrees of freedom」: the degrees of freedom for the p-value. Always NaN in this case.

Notes
-----
This code is modified from scipy.stats.ks_2samp and extended with supporting on
frequency data. See [1]_ for further information.

This tests whether 2 samples are drawn from the same distribution. Note
that, like in the case of the one-sample K-S test, the distribution is
assumed to be continuous.

This is the two-sided test, one-sided tests are not implemented.
The test uses the two-sided asymptotic Kolmogorov-Smirnov distribution.

If the K-S statistic is small or the p-value is high, then we cannot
reject the hypothesis that the distributions of the two samples
are the same.

References
----------
.. [1] scipy.stats.mstats.ks_2samp — SciPy Reference Guide
       http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mstats.ks_2samp.html
.. [2] Frank C. Porter. (2008). Testing Consistency of Two Histograms, arXiv:0804.0380 [physics.data-an].

Examples
--------
>>> from scipy import stats
>>> np.random.seed(12345678)  #fix random seed to get the same result
>>> n1 = 200  # size of first sample
>>> n2 = 300  # size of second sample

For a different distribution, we can reject the null hypothesis since the
pvalue is below 1%:

>>> rvs1 = stats.norm.rvs(size=n1, loc=0., scale=1)
>>> rvs2 = stats.norm.rvs(size=n2, loc=0.5, scale=1.5)
>>> stats.ks_2samp(rvs1, rvs2)
(0.20833333333333337, 4.6674975515806989e-005)

For a slightly different distribution, we cannot reject the null hypothesis
at a 10% or lower alpha since the p-value at 0.144 is higher than 10%

>>> rvs3 = stats.norm.rvs(size=n2, loc=0.01, scale=1.0)
>>> stats.ks_2samp(rvs1, rvs3)
(0.10333333333333333, 0.14498781825751686)

For an identical distribution, we cannot reject the null hypothesis since
the p-value is high, 41%:

>>> rvs4 = stats.norm.rvs(size=n2, loc=0.0, scale=1.0)
>>> stats.ks_2samp(rvs1, rvs4)
(0.07999999999999996, 0.41126949729859719)

Definition at line 22 of file test_statistics.py.

def ks_2samp(data1, data2, binned=False):
    """
    Computes the Kolmogorov-Smirnov statistic on 2 samples/frequencies.
 
    This is a two-sided test for the null hypothesis that 2 independent samples
    are drawn from the same continuous distribution.
 
    Parameters
    ----------
    data1, data2 : sequence of 1-D ndarrays
        Input data. Can be either observed frequencies in each category or array
        of sample observations.
    binned : bool, optional, default: False
        If True/False, assume the type of input data is observed frequencies/samples.
 
    Returns
    -------
    statistic : float
        KS statistic
    pvalue : float
        two-tailed p-value
    ndf : NaN
        「Degrees of freedom」: the degrees of freedom for the p-value. Always NaN in this case.
 
    Notes
    -----
    This code is modified from scipy.stats.ks_2samp and extended with supporting on
    frequency data. See [1]_ for further information.
 
    This tests whether 2 samples are drawn from the same distribution. Note
    that, like in the case of the one-sample K-S test, the distribution is
    assumed to be continuous.
 
    This is the two-sided test, one-sided tests are not implemented.
    The test uses the two-sided asymptotic Kolmogorov-Smirnov distribution.
 
    If the K-S statistic is small or the p-value is high, then we cannot
    reject the hypothesis that the distributions of the two samples
    are the same.
 
    References
    ----------
    .. [1] scipy.stats.mstats.ks_2samp — SciPy Reference Guide
           http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mstats.ks_2samp.html
    .. [2] Frank C. Porter. (2008). Testing Consistency of Two Histograms, arXiv:0804.0380 [physics.data-an].
 
    Examples
    --------
    >>> from scipy import stats
    >>> np.random.seed(12345678)  #fix random seed to get the same result
    >>> n1 = 200  # size of first sample
    >>> n2 = 300  # size of second sample
 
    For a different distribution, we can reject the null hypothesis since the
    pvalue is below 1%:
 
    >>> rvs1 = stats.norm.rvs(size=n1, loc=0., scale=1)
    >>> rvs2 = stats.norm.rvs(size=n2, loc=0.5, scale=1.5)
    >>> stats.ks_2samp(rvs1, rvs2)
    (0.20833333333333337, 4.6674975515806989e-005)
 
    For a slightly different distribution, we cannot reject the null hypothesis
    at a 10% or lower alpha since the p-value at 0.144 is higher than 10%
 
    >>> rvs3 = stats.norm.rvs(size=n2, loc=0.01, scale=1.0)
    >>> stats.ks_2samp(rvs1, rvs3)
    (0.10333333333333333, 0.14498781825751686)
 
    For an identical distribution, we cannot reject the null hypothesis since
    the p-value is high, 41%:
 
    >>> rvs4 = stats.norm.rvs(size=n2, loc=0.0, scale=1.0)
    >>> stats.ks_2samp(rvs1, rvs4)
    (0.07999999999999996, 0.41126949729859719)
 
    """
    if binned is True:
        cdf1 = np.cumsum(data1)
        cdf2 = np.cumsum(data2)
        n1 = cdf1[-1]
        n2 = cdf2[-1]
        cdf1 /= n1
        cdf2 /= n2
        ndf = data1.shape[0]
    else:
        n1 = data1.shape[0]
        n2 = data2.shape[0]
        ndf = float("nan")
        if binned is False:
            data1 = np.sort(data1)
            data2 = np.sort(data2)
            data_all = np.concatenate([data1, data2])
            cdf1 = np.searchsorted(data1, data_all, side='right') / (1.0*n1)
            cdf2 = np.searchsorted(data2, data_all, side='right') / (1.0*n2)
        if binned == 'kde':
            data1_kde = sm.nonparametric.KDEUnivariate(data1)
            data2_kde = sm.nonparametric.KDEUnivariate(data2)
            data1_kde.fit()
            data2_kde.fit()
            i, min_sup = min(enumerate([data1_kde.support, data1_kde.support]), key = lambda data: data[1].shape[0])
            if i == 0:
                cdf1 = data1_kde.cdf
                cdf2 = scipy.interpolate.interp1d(data2_kde.support, data2_kde.cdf)(min_sup)
            else:
                cdf1 = scipy.interpolate.interp1d(data1_kde.support, data1_kde.cdf)(min_sup)
                cdf2 = data2_kde.cdf
    d = np.max(np.absolute(cdf1 - cdf2))
    # Note: d absolute not signed distance
    en = np.sqrt(n1 * n2 / float(n1 + n2))
    try:
        prob = scipy.stats.distributions.kstwobign.sf((en + 0.12 + 0.11 / en) * d)
    except Exception:
        prob = 1.0
    if DEBUG:
        global statistic_seq
        statistic_seq = np.absolute(cdf1 - cdf2)
    return Ks_2sampResult(d, prob, ndf)
 
 

likelihoodratio_ksamp()

def python.test_statistics.likelihoodratio_ksamp	(		data1,
			data2,
			normed = True,
			binned = True
	)

The likelihood-ratio test tests the null hypothesis that 2 given frequencies are
drawn from the same distribution.

Parameters
----------
data1, data2 : sequence of 1-D ndarrays
    Input data. Observed frequencies in each category.
normed : bool, optional, default: True
    If True/False, shape/value comparison test.
binned : bool, optional, default: True
    If True/False, assume the type of input data is observed frequencies/samples.

Returns
-------
statistic : float
    The llike-ratio test statistic.
pvalue : float
    Corresponding p-value.
ndf : int
    「Degrees of freedom」: the degrees of freedom for the p-value. The p-value
    is computed using a chi-squared distribution with k - 1 degrees of freedom,
    where k is the number of observed frequencies without bins has zero counts 
    in both histograms.

Definition at line 581 of file test_statistics.py.

def likelihoodratio_ksamp(data1, data2, normed=True, binned=True):
    """
    The likelihood-ratio test tests the null hypothesis that 2 given frequencies are
    drawn from the same distribution.
    
    Parameters
    ----------
    data1, data2 : sequence of 1-D ndarrays
        Input data. Observed frequencies in each category.
    normed : bool, optional, default: True
        If True/False, shape/value comparison test.
    binned : bool, optional, default: True
        If True/False, assume the type of input data is observed frequencies/samples.
    
    Returns
    -------
    statistic : float
        The llike-ratio test statistic.
    pvalue : float
        Corresponding p-value.
    ndf : int
        「Degrees of freedom」: the degrees of freedom for the p-value. The p-value
        is computed using a chi-squared distribution with k - 1 degrees of freedom,
        where k is the number of observed frequencies without bins has zero counts 
        in both histograms.
    """
    not_all_zeros = (data1 != 0) & (data2 != 0)
    ndf = sum((data1 != 0) | (data2 != 0))
    N_data1 = data1.sum()
    N_data2 = data2.sum()
    if normed:
        ndf -= 1
    llike_H0 = _loglikelihood(data1[not_all_zeros], data2[not_all_zeros], N_data1, N_data2, ratio=True, H="H0") + _zloglikelihood(data1[~not_all_zeros], data2[~not_all_zeros], N_data1, N_data2, H="H0")
    if DEBUG:
        global statistic_seq
        STATISTIC_SEQ = statistic_seq
    llike_H1 = _loglikelihood(data1[not_all_zeros], data2[not_all_zeros], N_data1, N_data2, ratio=True, H="H1")
    if DEBUG:
        STATISTIC_SEQ[:statistic_seq.shape[0]] -= statistic_seq
        statistic_seq = STATISTIC_SEQ
    llike_ratio = llike_H0 - llike_H1
    return LikelihoodRatio_ksampResult(llike_ratio, scipy.stats.chi2.sf(llike_ratio, ndf), ndf)
 

likelihoodvalue_ksamp()

def python.test_statistics.likelihoodvalue_ksamp	(		data1,
			data2,
			normed = True,
			binned = True
	)

The likelihood-value test tests the null hypothesis that 2 given frequencies are
drawn from the same distribution.

Parameters
----------
data1, data2 : sequence of 1-D ndarrays
    Input data. Observed frequencies in each category.
normed : bool, optional, default: True
    If True/False, shape/value comparison test.
binned : bool, optional, default: True
    If True/False, assume the type of input data is observed frequencies/samples.

Returns
-------
statistic : float
    The llike-value test statistic.
pvalue : float
    Corresponding p-value.
ndf : int
    「Degrees of freedom」: the degrees of freedom for the p-value. The p-value
    is computed using a chi-squared distribution with k - 1 degrees of freedom,
    where k is the number of observed frequencies without bins has zero counts 
    in both histograms.

Definition at line 625 of file test_statistics.py.

def likelihoodvalue_ksamp(data1, data2, normed=True, binned=True):
    """
    The likelihood-value test tests the null hypothesis that 2 given frequencies are
    drawn from the same distribution.
    
    Parameters
    ----------
    data1, data2 : sequence of 1-D ndarrays
        Input data. Observed frequencies in each category.
    normed : bool, optional, default: True
        If True/False, shape/value comparison test.
    binned : bool, optional, default: True
        If True/False, assume the type of input data is observed frequencies/samples.
    
    Returns
    -------
    statistic : float
        The llike-value test statistic.
    pvalue : float
        Corresponding p-value.
    ndf : int
        「Degrees of freedom」: the degrees of freedom for the p-value. The p-value
        is computed using a chi-squared distribution with k - 1 degrees of freedom,
        where k is the number of observed frequencies without bins has zero counts 
        in both histograms.
    """
    no_zeros = (data1 != 0) | (data2 != 0)
    ndf = no_zeros.sum()
    if normed:
        ndf -= 1
    llike = _loglikelihood(data1[no_zeros], data2[no_zeros], data1.sum(), data2.sum(), ratio=False, H="H0") 
    return LikelihoodValue_ksampResult(llike, scipy.stats.chi2.sf(llike, ndf), ndf)

Variable Documentation

Anderson_ksampResult

python.test_statistics.Anderson_ksampResult = namedtuple('Anderson_ksampResult', ('statistic', 'pvalue', 'ndf'))

Definition at line 397 of file test_statistics.py.

BDM_2sampResult

python.test_statistics.BDM_2sampResult = namedtuple('BDM_2sampResult', ('statistic', 'pvalue', 'ndf'))

Definition at line 195 of file test_statistics.py.

Chi2_2sampResult

python.test_statistics.Chi2_2sampResult = namedtuple('Chi2_2sampResult', ('statistic', 'pvalue','ndf'))

Definition at line 141 of file test_statistics.py.

CVM_2sampResult

python.test_statistics.CVM_2sampResult = namedtuple('CVM_2sampResult', ('statistic', 'pvalue', 'ndf'))

Definition at line 239 of file test_statistics.py.

DEBUG

bool python.test_statistics.DEBUG = False

Definition at line 17 of file test_statistics.py.

Ks_2sampResult

python.test_statistics.Ks_2sampResult = namedtuple('Ks_2sampResult', ('statistic', 'pvalue','ndf'))

Definition at line 21 of file test_statistics.py.

LikelihoodRatio_ksampResult

python.test_statistics.LikelihoodRatio_ksampResult = namedtuple('LikelihoodRatio_ksampResult', ('statistic', 'pvalue', 'ndf'))

Definition at line 580 of file test_statistics.py.

LikelihoodValue_ksampResult

python.test_statistics.LikelihoodValue_ksampResult = namedtuple('LikelihoodValue_ksampResult', ('statistic', 'pvalue', 'ndf'))

Definition at line 624 of file test_statistics.py.

statistic_seq

list python.test_statistics.statistic_seq = []

Definition at line 19 of file test_statistics.py.