def __init__(self, n=None, alpha=None, beta=None, power=None, p=None):
# There should be a better way.
# This loop both checks the values for p and caclualtes a value needed
# for the calculation.
self._denom = 0
if isinstance(p, list):
len_p = len(p)
self.df = len_p * (len_p - 1) / 2.0
self._adjustment = 0
for i in range(len_p - 1):
if isinstance(p, list):
for j in range(i, len_p):
is_in_0_1(p[i][j],
'All values of p should be in [0, 1].')
is_in_0_1(p[j][i],
'All values of p should be in [0, 1].')
self._denom += ((p[i][j] - p[j][i])**2 /
(p[i][j] + p[j][i]))
else:
raise ValueError("Each list in `p` must be a "
"list numerics")
else:
raise ValueError("`p` must be a list of lists of numerics")
if n is not None:
is_integer(n, '`n` should be of type Int.')
self.n = n
# Set remaining variables.
super(StuartMaxwell, self).__init__(alpha=alpha,
beta=beta,
power=power)
def __init__(self,
n=None,
alpha=None,
beta=None,
power=None,
p_0=None,
p=None):
if isinstance(p, list):
for value in p:
is_in_0_1(value, 'All values of p should be in (0, 1).')
else:
raise ValueError("`p` must be a list of Numerics.")
self.p = p
if isinstance(p_0, list):
for value in p_0:
is_in_0_1(value, 'All values of p_0 should be in (0, 1).')
else:
raise ValueError("`p_0` must be a list of Numerics.")
self.p_0 = p_0
if not len(p) == len(p_0):
raise ValueError("`p_0` and `p` must have the same length.")
if n is not None:
is_integer(n, '`n` should be of type Int.')
self.n = n
# Set remaining variables.
super(Pearson, self).__init__(alpha=alpha, beta=beta, power=power)
def __init__(self,
n=None,
alpha=None,
beta=None,
power=None,
p=None,
p_0=None,
margin=None):
is_in_0_1(p, 'p')
self.p = p
is_in_0_1(p_0, 'p_0')
self.p_0 = p_0
if margin is not None:
is_in_0_1(margin + p_0, 'margin + p_0')
self.margin = margin
self.p_0 += margin
else:
self.margin = 0
if n is not None:
is_integer(n, 'n')
self.n = n
super(Binomial, self).__init__(alpha=alpha,
power=power,
beta=beta,
hypothesis='equality')
def __init__(self,
n=None,
epsilon=None,
stdev=None,
known_stdev=None,
alpha=None,
beta=None,
power=None,
hypothesis=None):
if n is not None:
is_integer(n, 'N')
self.n = n
else:
self.n = None
is_numeric(epsilon, 'epsilon')
self.epsilon = epsilon
is_positive(stdev, 'stdev')
self.stdev = stdev
self.theta = self.epsilon / self.stdev
if known_stdev is not None:
is_boolean(known_stdev, 'known_stdev')
else:
known_stdev = True
self.known_stdev = known_stdev
# Initialize the remaining arguments through the parent.
super(MeansBase, self).__init__(alpha=alpha,
power=power,
beta=beta,
hypothesis=hypothesis)
def __init__(self,
n=None,
alpha=None,
beta=None,
power=None,
p=None,
pi=None):
self._check_list(p, 'p')
if pi is None:
pi = [1 / float(len(p))] * len(p)
else:
self._check_list(pi, 'pi')
if n is not None:
is_integer(n, '`n` should be of type Int.')
self.n = n
num = 0
denom = 0
for stratum in range(len(p)):
rowsums = [sum(p[stratum][0]), sum(p[stratum][1])]
colsums = [
p[stratum][0][0] + p[stratum][1][0],
p[stratum][0][1] + p[stratum][1][1]
]
num += pi[stratum] * (p[stratum][0][0] - rowsums[0] * colsums[0])
denom += (pi[stratum] * rowsums[0] * rowsums[1] * colsums[0] *
colsums[1])
self.delta = num / math.sqrt(denom)
self.n = n
# Set remaining variables.
super(CMH, self).__init__(alpha=alpha, beta=beta, power=power)
def __init__(self,
n=None,
alpha=None,
beta=None,
power=None,
p_2=None,
p_3=None,
p_4=None):
if n is not None:
is_integer(n, '`n` should be of type Int.')
self.n = n
is_in_0_1(p_2, 'p_2 should be in [0, 1].')
is_in_0_1(p_3, 'p_3 should be in [0, 1].')
is_in_0_1(p_4, 'p_4 should be in [0, 1].')
self.p_2 = p_2
self.p_3 = p_3
self.p_4 = p_4
# Initialize the remaining arguments through the parent.
super(OneSample, self).__init__(alpha=alpha,
power=power,
beta=beta,
hypothesis=None)
def __init__(self,
n=None,
alpha=None,
beta=None,
power=None,
method=None,
dist=None,
seed=None,
**kwargs):
if n is not None:
is_integer(n, '`n` should be of type Int.')
self.n = n
if dist in dir(stats):
self.dist = getattr(stats, dist)(**kwargs)
if seed is None:
self.seed = self._SEED
else:
self.seed = seed
if method == 'anderson' or method == 'anderson-darling':
# Need to figure out how to do this right
raise ValueError('{} is not a valid method'.format(method))
elif method == 'kolmogorov-smirnov' or method == 'ks':
def norm_ks(rvs):
return stats.kstest(rvs, 'norm')
self.normal_test = norm_ks
elif method == 'kurt' or method == 'kurtosis':
self.normal_test = stats.kurtosistest
self._minN = 20
elif method == 'martinez-iglewicz':
raise ValueError('{} is not a valid method'.format(method))
elif method in ['normaltest', 'omnibus']:
self.normal_test = stats.normaltest
self._minN = 20
elif method == 'range':
raise ValueError('{} is not a valid method'.format(method))
elif method in ['shapiro', 'sw', 'shapiro-wilks']:
self.normal_test = stats.shapiro
elif method in ['skew', 'skewness']:
self.normal_test = stats.skewtest
self._minN = 8
else:
raise ValueError('{} is not a valid method'.format(method))
# Initialize the remaining arguments through the parent.
super(Normal, self).__init__(alpha=alpha,
power=power,
beta=beta,
hypothesis=None)
def __init__(self, n=None, alpha=None, beta=None, power=None,
p_1=None, p_2=None):
if n is not None:
is_integer(n, '`n` should be of type Int.')
self.n = n
is_in_0_1(p_1, 'p_1 should be in [0, 1].')
is_in_0_1(p_2, 'p_2 should be in [0, 1].')
self.p_1 = p_1
self.p_2 = p_2
# Initialize the remaining arguments through the parent.
super(Independance, self).__init__(alpha=alpha, power=power,
beta=beta, hypothesis=None)
def __init__(self, n=None, alpha=None, beta=None, power=None, p_01=None,
p_10=None):
is_in_0_1(p_01, 'p_01 should be in [0, 1].')
is_in_0_1(p_10, 'p_10 should be in [0, 1].')
if n is not None:
is_integer(n, '`n` should be of type Int.')
self.n = n
self.p_01 = p_01
self.p_10 = p_10
self.alpha_factor = math.sqrt(p_01 + p_10)
self.beta_factor = math.sqrt(p_10 + p_01 - (p_01 - p_10)**2)
# Set remaining variables.
super(McNemar, self).__init__(alpha=alpha, beta=beta, power=power)
def __init__(self, n=None, alpha=None, beta=None, power=None, p=None):
# This should be made much better (see CMH)
if isinstance(p, list):
for values in p:
if isinstance(values, list):
for value in values:
is_in_0_1(value,
'All values of p should be in (0, 1).')
else:
raise ValueError("Each list in `p` must be a list "
"of numerics")
else:
raise ValueError("`p` must be a list of lists of numerics")
self.p = p
# needed for degree of freedom calculations
rows = len(p)
cols = len(p[0])
self.df = (rows - 1) * (cols - 1)
self._denom = 0
# This will be much improved when I implement this as a matrix!
colsums = [0] * cols
for i in range(rows):
for j in range(cols):
colsums[j] += p[i][j]
for i in range(rows):
rowsum = sum(p[i])
for j in range(cols):
self._denom += ((p[i][j] - rowsum * colsums[j])**2 /
(rowsum * colsums[j]))
if n is not None:
is_integer(n, '`n` should be of type Int.')
self.n = n
# Set remaining variables.
super(PearsonIndependance, self).__init__(alpha=alpha,
beta=beta,
power=power)
def __init__(self,
n=None,
alpha=None,
beta=None,
power=None,
p_11=None,
p_12=None,
p_21=None,
p_22=None,
gamma=None,
stdev_1=None,
stdev_2=None):
if gamma is None:
is_in_0_1(p_11, 'p_11 should be in [0, 1].')
is_in_0_1(p_12, 'p_12 should be in [0, 1].')
is_in_0_1(p_21, 'p_21 should be in [0, 1].')
is_in_0_1(p_22, 'p_22 should be in [0, 1].')
gamma = (math.log(p_11 / (1 - p_11)) + math.log(p_12 /
(1 - p_12)) -
math.log(p_21 / (1 - p_21)) - math.log(p_22 / (1 - p_22)))
else:
is_numeric(gamma, '`gamma` should be a number.')
if n is not None:
is_integer(n, '`n` should be of type Int.')
self.n = n
is_positive(stdev_1, 'stdev_1')
self.stdev_1 = stdev_1
is_positive(stdev_2, 'stdev_2')
self.stdev_2 = stdev_2
self.theta = gamma / math.sqrt(stdev_1**2 + stdev_2**2)
# Set remaining variables.
super(CarryOverEffect, self).__init__(alpha=alpha,
beta=beta,
power=power)
def __init__(self,
delta=None,
stdev_wr=None,
stdev_wt=None,
stdev_br=None,
stdev_bt=None,
theta_BE=None,
m_plus=None,
alpha=None,
power=None,
beta=None):
is_numeric(delta, 'delta')
self.delta = delta
is_positive(stdev_wr, 'stdev_wr')
self.stdev_wr = stdev_wr
is_positive(stdev_wt, 'stdev_wt')
self.stdev_wt = stdev_wt
is_positive(stdev_bt, 'stdev_bt')
self.stdev_bt = stdev_bt
is_positive(stdev_br, 'stdev')
self.stdev_br = stdev_br
if theta_BE is None:
theta_BE = 1
else:
is_positive(theta_BE, 'theta_BE')
self.theta_BE = theta_BE
if m_plus is None:
m_plus = MAX_ITERATIONS
else:
is_integer(m_plus, 'm_plus')
self.m_plus = m_plus
# Initialize the remaining arguments through the parent.
super(InVitro, self).__init__(hypothesis="equivalence",
alpha=alpha,
beta=beta,
power=power)
def __init__(self, n=None, alpha=None, beta=None, power=None, method=None,
dist=None, compare_dist=None, seed=None, **kwargs):
if n is not None:
is_integer(n, '`n` should be of type Int.')
self.n = n
if dist in dir(stats):
self.dist = getattr(stats, dist)(**kwargs)
else:
raise ValueError('{} is not a valid distribution'.format(dist))
if compare_dist not in dir(stats):
raise ValueError('{} is not a valid distribution'.format(compare_dist))
if seed is None:
self.seed = self._SEED
else:
self.seed = seed
if method == 'anderson' or method == 'anderson-darling':
if compare_dist not in ["norm", "expon", "logistic", "gumbel",
"gumbel_l", "gumbel_r", "extreme1"]:
raise ValueError('{} is not a valid distribution'.format(compare_dist))
def dist_anderson(rvs):
return stats.anderson(rvs, dist=compare_dist)
self.distribution_test = dist_anderson
elif method == 'kolmogorov-smirnov' or method == 'ks':
def dist_ks(rvs):
return stats.kstest(rvs, dist=compare_dist)
self.distribution_test = dist_ks
# Initialize the remaining arguments through the parent.
super(Distribution, self).__init__(alpha=alpha, power=power,
beta=beta, hypothesis=None)