Source code for statsmodels.stats.proportion
"""
Tests and Confidence Intervals for Binomial Proportions
Created on Fri Mar 01 00:23:07 2013
Author: Josef Perktold
License: BSD-3
"""
from statsmodels.compat.python import lzip
from typing import Callable
import numpy as np
import pandas as pd
from scipy import optimize, stats
from statsmodels.stats.base import AllPairsResults, HolderTuple
from statsmodels.stats.weightstats import _zstat_generic2
from statsmodels.tools.sm_exceptions import HypothesisTestWarning
from statsmodels.tools.testing import Holder
from statsmodels.tools.validation import array_like
FLOAT_INFO = np.finfo(float)
def _bound_proportion_confint(
func: Callable[[float], float], qi: float, lower: bool = True
) -> float:
"""
Try hard to find a bound different from eps/1 - eps in proportion_confint
Parameters
----------
func : callable
Callable function to use as the objective of the search
qi : float
The empirical success rate
lower : bool
Whether to fund a lower bound for the left side of the CI
Returns
-------
float
The coarse bound
"""
default = FLOAT_INFO.eps if lower else 1.0 - FLOAT_INFO.eps
def step(v):
return v / 8 if lower else v + (1.0 - v) / 8
x = step(qi)
w = func(x)
cnt = 1
while w > 0 and cnt < 10:
x = step(x)
w = func(x)
cnt += 1
return x if cnt < 10 else default
def _bisection_search_conservative(
func: Callable[[float], float], lb: float, ub: float, steps: int = 27
) -> tuple[float, float]:
"""
Private function used as a fallback by proportion_confint
Used when brentq returns a non-conservative bound for the CI
Parameters
----------
func : callable
Callable function to use as the objective of the search
lb : float
Lower bound
ub : float
Upper bound
steps : int
Number of steps to use in the bisection
Returns
-------
est : float
The estimated value. Will always produce a negative value of func
func_val : float
The value of the function at the estimate
"""
upper = func(ub)
lower = func(lb)
best = upper if upper < 0 else lower
best_pt = ub if upper < 0 else lb
if np.sign(lower) == np.sign(upper):
raise ValueError("problem with signs")
mp = (ub + lb) / 2
mid = func(mp)
if (mid < 0) and (mid > best):
best = mid
best_pt = mp
for _ in range(steps):
if np.sign(mid) == np.sign(upper):
ub = mp
upper = mid
else:
lb = mp
mp = (ub + lb) / 2
mid = func(mp)
if (mid < 0) and (mid > best):
best = mid
best_pt = mp
return best_pt, best
[docs]
def proportion_confint(count, nobs, alpha:float=0.05, method="normal"):
"""
Confidence interval for a binomial proportion
Parameters
----------
count : {int or float, array_like}
number of successes, can be pandas Series or DataFrame. Arrays
must contain integer values if method is "binom_test".
nobs : {int or float, array_like}
total number of trials. Arrays must contain integer values if method
is "binom_test".
alpha : float
Significance level, default 0.05. Must be in (0, 1)
method : {"normal", "agresti_coull", "beta", "wilson", "binom_test"}
default: "normal"
method to use for confidence interval. Supported methods:
- `normal` : asymptotic normal approximation
- `agresti_coull` : Agresti-Coull interval
- `beta` : Clopper-Pearson interval based on Beta distribution
- `wilson` : Wilson Score interval
- `jeffreys` : Jeffreys Bayesian Interval
- `binom_test` : Numerical inversion of binom_test
Returns
-------
ci_low, ci_upp : {float, ndarray, Series DataFrame}
lower and upper confidence level with coverage (approximately) 1-alpha.
When a pandas object is returned, then the index is taken from `count`.
Notes
-----
Beta, the Clopper-Pearson exact interval has coverage at least 1-alpha,
but is in general conservative. Most of the other methods have average
coverage equal to 1-alpha, but will have smaller coverage in some cases.
The "beta" and "jeffreys" interval are central, they use alpha/2 in each
tail, and alpha is not adjusted at the boundaries. In the extreme case
when `count` is zero or equal to `nobs`, then the coverage will be only
1 - alpha/2 in the case of "beta".
The confidence intervals are clipped to be in the [0, 1] interval in the
case of "normal" and "agresti_coull".
Method "binom_test" directly inverts the binomial test in scipy.stats.
which has discrete steps.
TODO: binom_test intervals raise an exception in small samples if one
interval bound is close to zero or one.
References
----------
.. [*] https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval
.. [*] Brown, Lawrence D.; Cai, T. Tony; DasGupta, Anirban (2001).
"Interval Estimation for a Binomial Proportion", Statistical
Science 16 (2): 101–133. doi:10.1214/ss/1009213286.
"""
is_scalar = np.isscalar(count) and np.isscalar(nobs)
is_pandas = isinstance(count, (pd.Series, pd.DataFrame))
count_a = array_like(count, "count", optional=False, ndim=None)
nobs_a = array_like(nobs, "nobs", optional=False, ndim=None)
def _check(x: np.ndarray, name: str) -> np.ndarray:
if np.issubdtype(x.dtype, np.integer):
return x
y = x.astype(np.int64, casting="unsafe")
if np.any(y != x):
raise ValueError(
f"{name} must have an integral dtype. Found data with "
f"dtype {x.dtype}"
)
return y
if method == "binom_test":
count_a = _check(np.asarray(count_a), "count")
nobs_a = _check(np.asarray(nobs_a), "count")
q_ = count_a / nobs_a
alpha_2 = 0.5 * alpha
if method == "normal":
std_ = np.sqrt(q_ * (1 - q_) / nobs_a)
dist = stats.norm.isf(alpha / 2.0) * std_
ci_low = q_ - dist
ci_upp = q_ + dist
elif method == "binom_test":
# inverting the binomial test
def func_factory(count: int, nobs: int) -> Callable[[float], float]:
if hasattr(stats, "binomtest"):
def func(qi):
return stats.binomtest(count, nobs, p=qi).pvalue - alpha
else:
# Remove after min SciPy >= 1.7
def func(qi):
return stats.binom_test(count, nobs, p=qi) - alpha
return func
bcast = np.broadcast(count_a, nobs_a)
ci_low = np.zeros(bcast.shape)
ci_upp = np.zeros(bcast.shape)
index = bcast.index
for c, n in bcast:
# Enforce symmetry
reverse = False
_q = q_.flat[index]
if c > n // 2:
c = n - c
reverse = True
_q = 1 - _q
func = func_factory(c, n)
if c == 0:
ci_low.flat[index] = 0.0
else:
lower_bnd = _bound_proportion_confint(func, _q, lower=True)
val, _z = optimize.brentq(
func, lower_bnd, _q, full_output=True
)
if func(val) > 0:
power = 10
new_lb = val - (val - lower_bnd) / 2**power
while func(new_lb) > 0 and power >= 0:
power -= 1
new_lb = val - (val - lower_bnd) / 2**power
val, _ = _bisection_search_conservative(func, new_lb, _q)
ci_low.flat[index] = val
if c == n:
ci_upp.flat[index] = 1.0
else:
upper_bnd = _bound_proportion_confint(func, _q, lower=False)
val, _z = optimize.brentq(
func, _q, upper_bnd, full_output=True
)
if func(val) > 0:
power = 10
new_ub = val + (upper_bnd - val) / 2**power
while func(new_ub) > 0 and power >= 0:
power -= 1
new_ub = val - (upper_bnd - val) / 2**power
val, _ = _bisection_search_conservative(func, _q, new_ub)
ci_upp.flat[index] = val
if reverse:
temp = ci_upp.flat[index]
ci_upp.flat[index] = 1 - ci_low.flat[index]
ci_low.flat[index] = 1 - temp
index = bcast.index
elif method == "beta":
ci_low = stats.beta.ppf(alpha_2, count_a, nobs_a - count_a + 1)
ci_upp = stats.beta.isf(alpha_2, count_a + 1, nobs_a - count_a)
if np.ndim(ci_low) > 0:
ci_low.flat[q_.flat == 0] = 0
ci_upp.flat[q_.flat == 1] = 1
else:
ci_low = 0 if q_ == 0 else ci_low
ci_upp = 1 if q_ == 1 else ci_upp
elif method == "agresti_coull":
crit = stats.norm.isf(alpha / 2.0)
nobs_c = nobs_a + crit**2
q_c = (count_a + crit**2 / 2.0) / nobs_c
std_c = np.sqrt(q_c * (1.0 - q_c) / nobs_c)
dist = crit * std_c
ci_low = q_c - dist
ci_upp = q_c + dist
elif method == "wilson":
crit = stats.norm.isf(alpha / 2.0)
crit2 = crit**2
denom = 1 + crit2 / nobs_a
center = (q_ + crit2 / (2 * nobs_a)) / denom
dist = crit * np.sqrt(
q_ * (1.0 - q_) / nobs_a + crit2 / (4.0 * nobs_a**2)
)
dist /= denom
ci_low = center - dist
ci_upp = center + dist
# method adjusted to be more forgiving of misspellings or incorrect option name
elif method[:4] == "jeff":
ci_low, ci_upp = stats.beta.interval(
1 - alpha, count_a + 0.5, nobs_a - count_a + 0.5
)
else:
raise NotImplementedError(f"method {method} is not available")
if method in ["normal", "agresti_coull"]:
ci_low = np.clip(ci_low, 0, 1)
ci_upp = np.clip(ci_upp, 0, 1)
if is_pandas:
container = pd.Series if isinstance(count, pd.Series) else pd.DataFrame
ci_low = container(ci_low, index=count.index)
ci_upp = container(ci_upp, index=count.index)
if is_scalar:
return float(ci_low), float(ci_upp)
return ci_low, ci_upp
[docs]
def multinomial_proportions_confint(counts, alpha=0.05, method='goodman'):
"""
Confidence intervals for multinomial proportions.
Parameters
----------
counts : array_like of int, 1-D
Number of observations in each category.
alpha : float in (0, 1), optional
Significance level, defaults to 0.05.
method : {'goodman', 'sison-glaz'}, optional
Method to use to compute the confidence intervals; available methods
are:
- `goodman`: based on a chi-squared approximation, valid if all
values in `counts` are greater or equal to 5 [2]_
- `sison-glaz`: less conservative than `goodman`, but only valid if
`counts` has 7 or more categories (``len(counts) >= 7``) [3]_
Returns
-------
confint : ndarray, 2-D
Array of [lower, upper] confidence levels for each category, such that
overall coverage is (approximately) `1-alpha`.
Raises
------
ValueError
If `alpha` is not in `(0, 1)` (bounds excluded), or if the values in
`counts` are not all positive or null.
NotImplementedError
If `method` is not kown.
Exception
When ``method == 'sison-glaz'``, if for some reason `c` cannot be
computed; this signals a bug and should be reported.
Notes
-----
The `goodman` method [2]_ is based on approximating a statistic based on
the multinomial as a chi-squared random variable. The usual recommendation
is that this is valid if all the values in `counts` are greater than or
equal to 5. There is no condition on the number of categories for this
method.
The `sison-glaz` method [3]_ approximates the multinomial probabilities,
and evaluates that with a maximum-likelihood estimator. The first
approximation is an Edgeworth expansion that converges when the number of
categories goes to infinity, and the maximum-likelihood estimator converges
when the number of observations (``sum(counts)``) goes to infinity. In
their paper, Sison & Glaz demo their method with at least 7 categories, so
``len(counts) >= 7`` with all values in `counts` at or above 5 can be used
as a rule of thumb for the validity of this method. This method is less
conservative than the `goodman` method (i.e. it will yield confidence
intervals closer to the desired significance level), but produces
confidence intervals of uniform width over all categories (except when the
intervals reach 0 or 1, in which case they are truncated), which makes it
most useful when proportions are of similar magnitude.
Aside from the original sources ([1]_, [2]_, and [3]_), the implementation
uses the formulas (though not the code) presented in [4]_ and [5]_.
References
----------
.. [1] Levin, Bruce, "A representation for multinomial cumulative
distribution functions," The Annals of Statistics, Vol. 9, No. 5,
1981, pp. 1123-1126.
.. [2] Goodman, L.A., "On simultaneous confidence intervals for multinomial
proportions," Technometrics, Vol. 7, No. 2, 1965, pp. 247-254.
.. [3] Sison, Cristina P., and Joseph Glaz, "Simultaneous Confidence
Intervals and Sample Size Determination for Multinomial
Proportions," Journal of the American Statistical Association,
Vol. 90, No. 429, 1995, pp. 366-369.
.. [4] May, Warren L., and William D. Johnson, "A SAS® macro for
constructing simultaneous confidence intervals for multinomial
proportions," Computer methods and programs in Biomedicine, Vol. 53,
No. 3, 1997, pp. 153-162.
.. [5] May, Warren L., and William D. Johnson, "Constructing two-sided
simultaneous confidence intervals for multinomial proportions for
small counts in a large number of cells," Journal of Statistical
Software, Vol. 5, No. 6, 2000, pp. 1-24.
"""
if alpha <= 0 or alpha >= 1:
raise ValueError('alpha must be in (0, 1), bounds excluded')
counts = np.array(counts, dtype=float)
if (counts < 0).any():
raise ValueError('counts must be >= 0')
n = counts.sum()
k = len(counts)
proportions = counts / n
if method == 'goodman':
chi2 = stats.chi2.ppf(1 - alpha / k, 1)
delta = chi2 ** 2 + (4 * n * proportions * chi2 * (1 - proportions))
region = ((2 * n * proportions + chi2 +
np.array([- np.sqrt(delta), np.sqrt(delta)])) /
(2 * (chi2 + n))).T
elif method[:5] == 'sison': # We accept any name starting with 'sison'
# Define a few functions we'll use a lot.
def poisson_interval(interval, p):
"""
Compute P(b <= Z <= a) where Z ~ Poisson(p) and
`interval = (b, a)`.
"""
b, a = interval
prob = stats.poisson.cdf(a, p) - stats.poisson.cdf(b - 1, p)
return prob
def truncated_poisson_factorial_moment(interval, r, p):
"""
Compute mu_r, the r-th factorial moment of a poisson random
variable of parameter `p` truncated to `interval = (b, a)`.
"""
b, a = interval
return p ** r * (1 - ((poisson_interval((a - r + 1, a), p) -
poisson_interval((b - r, b - 1), p)) /
poisson_interval((b, a), p)))
def edgeworth(intervals):
"""
Compute the Edgeworth expansion term of Sison & Glaz's formula
(1) (approximated probability for multinomial proportions in a
given box).
"""
# Compute means and central moments of the truncated poisson
# variables.
mu_r1, mu_r2, mu_r3, mu_r4 = (
np.array([truncated_poisson_factorial_moment(interval, r, p)
for (interval, p) in zip(intervals, counts)])
for r in range(1, 5)
)
mu = mu_r1
mu2 = mu_r2 + mu - mu ** 2
mu3 = mu_r3 + mu_r2 * (3 - 3 * mu) + mu - 3 * mu ** 2 + 2 * mu ** 3
mu4 = (mu_r4 + mu_r3 * (6 - 4 * mu) +
mu_r2 * (7 - 12 * mu + 6 * mu ** 2) +
mu - 4 * mu ** 2 + 6 * mu ** 3 - 3 * mu ** 4)
# Compute expansion factors, gamma_1 and gamma_2.
g1 = mu3.sum() / mu2.sum() ** 1.5
g2 = (mu4.sum() - 3 * (mu2 ** 2).sum()) / mu2.sum() ** 2
# Compute the expansion itself.
x = (n - mu.sum()) / np.sqrt(mu2.sum())
phi = np.exp(- x ** 2 / 2) / np.sqrt(2 * np.pi)
H3 = x ** 3 - 3 * x
H4 = x ** 4 - 6 * x ** 2 + 3
H6 = x ** 6 - 15 * x ** 4 + 45 * x ** 2 - 15
f = phi * (1 + g1 * H3 / 6 + g2 * H4 / 24 + g1 ** 2 * H6 / 72)
return f / np.sqrt(mu2.sum())
def approximated_multinomial_interval(intervals):
"""
Compute approximated probability for Multinomial(n, proportions)
to be in `intervals` (Sison & Glaz's formula (1)).
"""
return np.exp(
np.sum(np.log([poisson_interval(interval, p)
for (interval, p) in zip(intervals, counts)])) +
np.log(edgeworth(intervals)) -
np.log(stats.poisson._pmf(n, n))
)
def nu(c):
"""
Compute interval coverage for a given `c` (Sison & Glaz's
formula (7)).
"""
return approximated_multinomial_interval(
[(np.maximum(count - c, 0), np.minimum(count + c, n))
for count in counts])
# Find the value of `c` that will give us the confidence intervals
# (solving nu(c) <= 1 - alpha < nu(c + 1).
c = 1.0
nuc = nu(c)
nucp1 = nu(c + 1)
while not (nuc <= (1 - alpha) < nucp1):
if c > n:
raise Exception("Couldn't find a value for `c` that "
"solves nu(c) <= 1 - alpha < nu(c + 1)")
c += 1
nuc = nucp1
nucp1 = nu(c + 1)
# Compute gamma and the corresponding confidence intervals.
g = (1 - alpha - nuc) / (nucp1 - nuc)
ci_lower = np.maximum(proportions - c / n, 0)
ci_upper = np.minimum(proportions + (c + 2 * g) / n, 1)
region = np.array([ci_lower, ci_upper]).T
else:
raise NotImplementedError('method "%s" is not available' % method)
return region
[docs]
def samplesize_confint_proportion(proportion, half_length, alpha=0.05,
method='normal'):
"""
Find sample size to get desired confidence interval length
Parameters
----------
proportion : float in (0, 1)
proportion or quantile
half_length : float in (0, 1)
desired half length of the confidence interval
alpha : float in (0, 1)
significance level, default 0.05,
coverage of the two-sided interval is (approximately) ``1 - alpha``
method : str in ['normal']
method to use for confidence interval,
currently only normal approximation
Returns
-------
n : float
sample size to get the desired half length of the confidence interval
Notes
-----
this is mainly to store the formula.
possible application: number of replications in bootstrap samples
"""
q_ = proportion
if method == 'normal':
n = q_ * (1 - q_) / (half_length / stats.norm.isf(alpha / 2.))**2
else:
raise NotImplementedError('only "normal" is available')
return n
[docs]
def proportion_effectsize(prop1, prop2, method='normal'):
"""
Effect size for a test comparing two proportions
for use in power function
Parameters
----------
prop1, prop2 : float or array_like
The proportion value(s).
Returns
-------
es : float or ndarray
effect size for (transformed) prop1 - prop2
Notes
-----
only method='normal' is implemented to match pwr.p2.test
see http://www.statmethods.net/stats/power.html
Effect size for `normal` is defined as ::
2 * (arcsin(sqrt(prop1)) - arcsin(sqrt(prop2)))
I think other conversions to normality can be used, but I need to check.
Examples
--------
>>> import statsmodels.api as sm
>>> sm.stats.proportion_effectsize(0.5, 0.4)
0.20135792079033088
>>> sm.stats.proportion_effectsize([0.3, 0.4, 0.5], 0.4)
array([-0.21015893, 0. , 0.20135792])
"""
if method != 'normal':
raise ValueError('only "normal" is implemented')
es = 2 * (np.arcsin(np.sqrt(prop1)) - np.arcsin(np.sqrt(prop2)))
return es
def std_prop(prop, nobs):
"""
Standard error for the estimate of a proportion
This is just ``np.sqrt(p * (1. - p) / nobs)``
Parameters
----------
prop : array_like
proportion
nobs : int, array_like
number of observations
Returns
-------
std : array_like
standard error for a proportion of nobs independent observations
"""
return np.sqrt(prop * (1. - prop) / nobs)
def _std_diff_prop(p1, p2, ratio=1):
return np.sqrt(p1 * (1 - p1) + p2 * (1 - p2) / ratio)
def _power_ztost(mean_low, var_low, mean_upp, var_upp, mean_alt, var_alt,
alpha=0.05, discrete=True, dist='norm', nobs=None,
continuity=0, critval_continuity=0):
"""
Generic statistical power function for normal based equivalence test
This includes options to adjust the normal approximation and can use
the binomial to evaluate the probability of the rejection region
see power_ztost_prob for a description of the options
"""
# TODO: refactor structure, separate norm and binom better
if not isinstance(continuity, tuple):
continuity = (continuity, continuity)
crit = stats.norm.isf(alpha)
k_low = mean_low + np.sqrt(var_low) * crit
k_upp = mean_upp - np.sqrt(var_upp) * crit
if discrete or dist == 'binom':
k_low = np.ceil(k_low * nobs + 0.5 * critval_continuity)
k_upp = np.trunc(k_upp * nobs - 0.5 * critval_continuity)
if dist == 'norm':
#need proportion
k_low = (k_low) * 1. / nobs #-1 to match PASS
k_upp = k_upp * 1. / nobs
# else:
# if dist == 'binom':
# #need counts
# k_low *= nobs
# k_upp *= nobs
#print mean_low, np.sqrt(var_low), crit, var_low
#print mean_upp, np.sqrt(var_upp), crit, var_upp
if np.any(k_low > k_upp): #vectorize
import warnings
warnings.warn("no overlap, power is zero", HypothesisTestWarning)
std_alt = np.sqrt(var_alt)
z_low = (k_low - mean_alt - continuity[0] * 0.5 / nobs) / std_alt
z_upp = (k_upp - mean_alt + continuity[1] * 0.5 / nobs) / std_alt
if dist == 'norm':
power = stats.norm.cdf(z_upp) - stats.norm.cdf(z_low)
elif dist == 'binom':
power = (stats.binom.cdf(k_upp, nobs, mean_alt) -
stats.binom.cdf(k_low-1, nobs, mean_alt))
return power, (k_low, k_upp, z_low, z_upp)
[docs]
def binom_tost(count, nobs, low, upp):
"""
Exact TOST test for one proportion using binomial distribution
Parameters
----------
count : {int, array_like}
the number of successes in nobs trials.
nobs : int
the number of trials or observations.
low, upp : floats
lower and upper limit of equivalence region
Returns
-------
pvalue : float
p-value of equivalence test
pval_low, pval_upp : floats
p-values of lower and upper one-sided tests
"""
# binom_test_stat only returns pval
tt1 = binom_test(count, nobs, alternative='larger', prop=low)
tt2 = binom_test(count, nobs, alternative='smaller', prop=upp)
return np.maximum(tt1, tt2), tt1, tt2,
[docs]
def binom_tost_reject_interval(low, upp, nobs, alpha=0.05):
"""
Rejection region for binomial TOST
The interval includes the end points,
`reject` if and only if `r_low <= x <= r_upp`.
The interval might be empty with `r_upp < r_low`.
Parameters
----------
low, upp : floats
lower and upper limit of equivalence region
nobs : int
the number of trials or observations.
Returns
-------
x_low, x_upp : float
lower and upper bound of rejection region
"""
x_low = stats.binom.isf(alpha, nobs, low) + 1
x_upp = stats.binom.ppf(alpha, nobs, upp) - 1
return x_low, x_upp
[docs]
def binom_test_reject_interval(value, nobs, alpha=0.05, alternative='two-sided'):
"""
Rejection region for binomial test for one sample proportion
The interval includes the end points of the rejection region.
Parameters
----------
value : float
proportion under the Null hypothesis
nobs : int
the number of trials or observations.
Returns
-------
x_low, x_upp : int
lower and upper bound of rejection region
"""
if alternative in ['2s', 'two-sided']:
alternative = '2s' # normalize alternative name
alpha = alpha / 2
if alternative in ['2s', 'smaller']:
x_low = stats.binom.ppf(alpha, nobs, value) - 1
else:
x_low = 0
if alternative in ['2s', 'larger']:
x_upp = stats.binom.isf(alpha, nobs, value) + 1
else :
x_upp = nobs
return int(x_low), int(x_upp)
[docs]
def binom_test(count, nobs, prop=0.5, alternative='two-sided'):
"""
Perform a test that the probability of success is p.
This is an exact, two-sided test of the null hypothesis
that the probability of success in a Bernoulli experiment
is `p`.
Parameters
----------
count : {int, array_like}
the number of successes in nobs trials.
nobs : int
the number of trials or observations.
prop : float, optional
The probability of success under the null hypothesis,
`0 <= prop <= 1`. The default value is `prop = 0.5`
alternative : str in ['two-sided', 'smaller', 'larger']
alternative hypothesis, which can be two-sided or either one of the
one-sided tests.
Returns
-------
p-value : float
The p-value of the hypothesis test
Notes
-----
This uses scipy.stats.binom_test for the two-sided alternative.
"""
if np.any(prop > 1.0) or np.any(prop < 0.0):
raise ValueError("p must be in range [0,1]")
if alternative in ['2s', 'two-sided']:
try:
pval = stats.binomtest(count, n=nobs, p=prop).pvalue
except AttributeError:
# Remove after min SciPy >= 1.7
pval = stats.binom_test(count, n=nobs, p=prop)
elif alternative in ['l', 'larger']:
pval = stats.binom.sf(count-1, nobs, prop)
elif alternative in ['s', 'smaller']:
pval = stats.binom.cdf(count, nobs, prop)
else:
raise ValueError('alternative not recognized\n'
'should be two-sided, larger or smaller')
return pval
[docs]
def power_binom_tost(low, upp, nobs, p_alt=None, alpha=0.05):
if p_alt is None:
p_alt = 0.5 * (low + upp)
x_low, x_upp = binom_tost_reject_interval(low, upp, nobs, alpha=alpha)
power = (stats.binom.cdf(x_upp, nobs, p_alt) -
stats.binom.cdf(x_low-1, nobs, p_alt))
return power
[docs]
def power_ztost_prop(low, upp, nobs, p_alt, alpha=0.05, dist='norm',
variance_prop=None, discrete=True, continuity=0,
critval_continuity=0):
"""
Power of proportions equivalence test based on normal distribution
Parameters
----------
low, upp : floats
lower and upper limit of equivalence region
nobs : int
number of observations
p_alt : float in (0,1)
proportion under the alternative
alpha : float in (0,1)
significance level of the test
dist : str in ['norm', 'binom']
This defines the distribution to evaluate the power of the test. The
critical values of the TOST test are always based on the normal
approximation, but the distribution for the power can be either the
normal (default) or the binomial (exact) distribution.
variance_prop : None or float in (0,1)
If this is None, then the variances for the two one sided tests are
based on the proportions equal to the equivalence limits.
If variance_prop is given, then it is used to calculate the variance
for the TOST statistics. If this is based on an sample, then the
estimated proportion can be used.
discrete : bool
If true, then the critical values of the rejection region are converted
to integers. If dist is "binom", this is automatically assumed.
If discrete is false, then the TOST critical values are used as
floating point numbers, and the power is calculated based on the
rejection region that is not discretized.
continuity : bool or float
adjust the rejection region for the normal power probability. This has
and effect only if ``dist='norm'``
critval_continuity : bool or float
If this is non-zero, then the critical values of the tost rejection
region are adjusted before converting to integers. This affects both
distributions, ``dist='norm'`` and ``dist='binom'``.
Returns
-------
power : float
statistical power of the equivalence test.
(k_low, k_upp, z_low, z_upp) : tuple of floats
critical limits in intermediate steps
temporary return, will be changed
Notes
-----
In small samples the power for the ``discrete`` version, has a sawtooth
pattern as a function of the number of observations. As a consequence,
small changes in the number of observations or in the normal approximation
can have a large effect on the power.
``continuity`` and ``critval_continuity`` are added to match some results
of PASS, and are mainly to investigate the sensitivity of the ztost power
to small changes in the rejection region. From my interpretation of the
equations in the SAS manual, both are zero in SAS.
works vectorized
**verification:**
The ``dist='binom'`` results match PASS,
The ``dist='norm'`` results look reasonable, but no benchmark is available.
References
----------
SAS Manual: Chapter 68: The Power Procedure, Computational Resources
PASS Chapter 110: Equivalence Tests for One Proportion.
"""
mean_low = low
var_low = std_prop(low, nobs)**2
mean_upp = upp
var_upp = std_prop(upp, nobs)**2
mean_alt = p_alt
var_alt = std_prop(p_alt, nobs)**2
if variance_prop is not None:
var_low = var_upp = std_prop(variance_prop, nobs)**2
power = _power_ztost(mean_low, var_low, mean_upp, var_upp, mean_alt, var_alt,
alpha=alpha, discrete=discrete, dist=dist, nobs=nobs,
continuity=continuity, critval_continuity=critval_continuity)
return np.maximum(power[0], 0), power[1:]
def _table_proportion(count, nobs):
"""
Create a k by 2 contingency table for proportion
helper function for proportions_chisquare
Parameters
----------
count : {int, array_like}
the number of successes in nobs trials.
nobs : int
the number of trials or observations.
Returns
-------
table : ndarray
(k, 2) contingency table
Notes
-----
recent scipy has more elaborate contingency table functions
"""
count = np.asarray(count)
dt = np.promote_types(count.dtype, np.float64)
count = np.asarray(count, dtype=dt)
table = np.column_stack((count, nobs - count))
expected = table.sum(0) * table.sum(1)[:, None] * 1. / table.sum()
n_rows = table.shape[0]
return table, expected, n_rows
[docs]
def proportions_ztest(count, nobs, value=None, alternative='two-sided',
prop_var=False):
"""
Test for proportions based on normal (z) test
Parameters
----------
count : {int, array_like}
the number of successes in nobs trials. If this is array_like, then
the assumption is that this represents the number of successes for
each independent sample
nobs : {int, array_like}
the number of trials or observations, with the same length as
count.
value : float, array_like or None, optional
This is the value of the null hypothesis equal to the proportion in the
case of a one sample test. In the case of a two-sample test, the
null hypothesis is that prop[0] - prop[1] = value, where prop is the
proportion in the two samples. If not provided value = 0 and the null
is prop[0] = prop[1]
alternative : str in ['two-sided', 'smaller', 'larger']
The alternative hypothesis can be either two-sided or one of the one-
sided tests, smaller means that the alternative hypothesis is
``prop < value`` and larger means ``prop > value``. In the two sample
test, smaller means that the alternative hypothesis is ``p1 < p2`` and
larger means ``p1 > p2`` where ``p1`` is the proportion of the first
sample and ``p2`` of the second one.
prop_var : False or float in (0, 1)
If prop_var is false, then the variance of the proportion estimate is
calculated based on the sample proportion. Alternatively, a proportion
can be specified to calculate this variance. Common use case is to
use the proportion under the Null hypothesis to specify the variance
of the proportion estimate.
Returns
-------
zstat : float
test statistic for the z-test
p-value : float
p-value for the z-test
Examples
--------
>>> count = 5
>>> nobs = 83
>>> value = .05
>>> stat, pval = proportions_ztest(count, nobs, value)
>>> print('{0:0.3f}'.format(pval))
0.695
>>> import numpy as np
>>> from statsmodels.stats.proportion import proportions_ztest
>>> count = np.array([5, 12])
>>> nobs = np.array([83, 99])
>>> stat, pval = proportions_ztest(count, nobs)
>>> print('{0:0.3f}'.format(pval))
0.159
Notes
-----
This uses a simple normal test for proportions. It should be the same as
running the mean z-test on the data encoded 1 for event and 0 for no event
so that the sum corresponds to the count.
In the one and two sample cases with two-sided alternative, this test
produces the same p-value as ``proportions_chisquare``, since the
chisquare is the distribution of the square of a standard normal
distribution.
"""
# TODO: verify that this really holds
# TODO: add continuity correction or other improvements for small samples
# TODO: change options similar to propotion_ztost ?
count = np.asarray(count)
nobs = np.asarray(nobs)
if nobs.size == 1:
nobs = nobs * np.ones_like(count)
prop = count * 1. / nobs
k_sample = np.size(prop)
if value is None:
if k_sample == 1:
raise ValueError('value must be provided for a 1-sample test')
value = 0
if k_sample == 1:
diff = prop - value
elif k_sample == 2:
diff = prop[0] - prop[1] - value
else:
msg = 'more than two samples are not implemented yet'
raise NotImplementedError(msg)
p_pooled = np.sum(count) * 1. / np.sum(nobs)
nobs_fact = np.sum(1. / nobs)
if prop_var:
p_pooled = prop_var
var_ = p_pooled * (1 - p_pooled) * nobs_fact
std_diff = np.sqrt(var_)
from statsmodels.stats.weightstats import _zstat_generic2
return _zstat_generic2(diff, std_diff, alternative)
[docs]
def proportions_ztost(count, nobs, low, upp, prop_var='sample'):
"""
Equivalence test based on normal distribution
Parameters
----------
count : {int, array_like}
the number of successes in nobs trials. If this is array_like, then
the assumption is that this represents the number of successes for
each independent sample
nobs : int
the number of trials or observations, with the same length as
count.
low, upp : float
equivalence interval low < prop1 - prop2 < upp
prop_var : str or float in (0, 1)
prop_var determines which proportion is used for the calculation
of the standard deviation of the proportion estimate
The available options for string are 'sample' (default), 'null' and
'limits'. If prop_var is a float, then it is used directly.
Returns
-------
pvalue : float
pvalue of the non-equivalence test
t1, pv1 : tuple of floats
test statistic and pvalue for lower threshold test
t2, pv2 : tuple of floats
test statistic and pvalue for upper threshold test
Notes
-----
checked only for 1 sample case
"""
if prop_var == 'limits':
prop_var_low = low
prop_var_upp = upp
elif prop_var == 'sample':
prop_var_low = prop_var_upp = False #ztest uses sample
elif prop_var == 'null':
prop_var_low = prop_var_upp = 0.5 * (low + upp)
elif np.isreal(prop_var):
prop_var_low = prop_var_upp = prop_var
tt1 = proportions_ztest(count, nobs, alternative='larger',
prop_var=prop_var_low, value=low)
tt2 = proportions_ztest(count, nobs, alternative='smaller',
prop_var=prop_var_upp, value=upp)
return np.maximum(tt1[1], tt2[1]), tt1, tt2,
[docs]
def proportions_chisquare(count, nobs, value=None):
"""
Test for proportions based on chisquare test
Parameters
----------
count : {int, array_like}
the number of successes in nobs trials. If this is array_like, then
the assumption is that this represents the number of successes for
each independent sample
nobs : int
the number of trials or observations, with the same length as
count.
value : None or float or array_like
Returns
-------
chi2stat : float
test statistic for the chisquare test
p-value : float
p-value for the chisquare test
(table, expected)
table is a (k, 2) contingency table, ``expected`` is the corresponding
table of counts that are expected under independence with given
margins
Notes
-----
Recent version of scipy.stats have a chisquare test for independence in
contingency tables.
This function provides a similar interface to chisquare tests as
``prop.test`` in R, however without the option for Yates continuity
correction.
count can be the count for the number of events for a single proportion,
or the counts for several independent proportions. If value is given, then
all proportions are jointly tested against this value. If value is not
given and count and nobs are not scalar, then the null hypothesis is
that all samples have the same proportion.
"""
nobs = np.atleast_1d(nobs)
table, expected, n_rows = _table_proportion(count, nobs)
if value is not None:
expected = np.column_stack((nobs * value, nobs * (1 - value)))
ddof = n_rows - 1
else:
ddof = n_rows
#print table, expected
chi2stat, pval = stats.chisquare(table.ravel(), expected.ravel(),
ddof=ddof)
return chi2stat, pval, (table, expected)
[docs]
def proportions_chisquare_allpairs(count, nobs, multitest_method='hs'):
"""
Chisquare test of proportions for all pairs of k samples
Performs a chisquare test for proportions for all pairwise comparisons.
The alternative is two-sided
Parameters
----------
count : {int, array_like}
the number of successes in nobs trials.
nobs : int
the number of trials or observations.
multitest_method : str
This chooses the method for the multiple testing p-value correction,
that is used as default in the results.
It can be any method that is available in ``multipletesting``.
The default is Holm-Sidak 'hs'.
Returns
-------
result : AllPairsResults instance
The returned results instance has several statistics, such as p-values,
attached, and additional methods for using a non-default
``multitest_method``.
Notes
-----
Yates continuity correction is not available.
"""
#all_pairs = lmap(list, lzip(*np.triu_indices(4, 1)))
all_pairs = lzip(*np.triu_indices(len(count), 1))
pvals = [proportions_chisquare(count[list(pair)], nobs[list(pair)])[1]
for pair in all_pairs]
return AllPairsResults(pvals, all_pairs, multitest_method=multitest_method)
[docs]
def proportions_chisquare_pairscontrol(count, nobs, value=None,
multitest_method='hs', alternative='two-sided'):
"""
Chisquare test of proportions for pairs of k samples compared to control
Performs a chisquare test for proportions for pairwise comparisons with a
control (Dunnet's test). The control is assumed to be the first element
of ``count`` and ``nobs``. The alternative is two-sided, larger or
smaller.
Parameters
----------
count : {int, array_like}
the number of successes in nobs trials.
nobs : int
the number of trials or observations.
multitest_method : str
This chooses the method for the multiple testing p-value correction,
that is used as default in the results.
It can be any method that is available in ``multipletesting``.
The default is Holm-Sidak 'hs'.
alternative : str in ['two-sided', 'smaller', 'larger']
alternative hypothesis, which can be two-sided or either one of the
one-sided tests.
Returns
-------
result : AllPairsResults instance
The returned results instance has several statistics, such as p-values,
attached, and additional methods for using a non-default
``multitest_method``.
Notes
-----
Yates continuity correction is not available.
``value`` and ``alternative`` options are not yet implemented.
"""
if (value is not None) or (alternative not in ['two-sided', '2s']):
raise NotImplementedError
#all_pairs = lmap(list, lzip(*np.triu_indices(4, 1)))
all_pairs = [(0, k) for k in range(1, len(count))]
pvals = [proportions_chisquare(count[list(pair)], nobs[list(pair)],
#alternative=alternative)[1]
)[1]
for pair in all_pairs]
return AllPairsResults(pvals, all_pairs, multitest_method=multitest_method)
[docs]
def confint_proportions_2indep(count1, nobs1, count2, nobs2, method=None,
compare='diff', alpha=0.05, correction=True):
"""
Confidence intervals for comparing two independent proportions.
This assumes that we have two independent binomial samples.
Parameters
----------
count1, nobs1 : float
Count and sample size for first sample.
count2, nobs2 : float
Count and sample size for the second sample.
method : str
Method for computing confidence interval. If method is None, then a
default method is used. The default might change as more methods are
added.
diff:
- 'wald',
- 'agresti-caffo'
- 'newcomb' (default)
- 'score'
ratio:
- 'log'
- 'log-adjusted' (default)
- 'score'
odds-ratio:
- 'logit'
- 'logit-adjusted' (default)
- 'score'
compare : string in ['diff', 'ratio' 'odds-ratio']
If compare is diff, then the confidence interval is for diff = p1 - p2.
If compare is ratio, then the confidence interval is for the risk ratio
defined by ratio = p1 / p2.
If compare is odds-ratio, then the confidence interval is for the
odds-ratio defined by or = p1 / (1 - p1) / (p2 / (1 - p2).
alpha : float
Significance level for the confidence interval, default is 0.05.
The nominal coverage probability is 1 - alpha.
Returns
-------
low, upp
See Also
--------
test_proportions_2indep
tost_proportions_2indep
Notes
-----
Status: experimental, API and defaults might still change.
more ``methods`` will be added.
References
----------
.. [1] Fagerland, Morten W., Stian Lydersen, and Petter Laake. 2015.
“Recommended Confidence Intervals for Two Independent Binomial
Proportions.” Statistical Methods in Medical Research 24 (2): 224–54.
https://doi.org/10.1177/0962280211415469.
.. [2] Koopman, P. A. R. 1984. “Confidence Intervals for the Ratio of Two
Binomial Proportions.” Biometrics 40 (2): 513–17.
https://doi.org/10.2307/2531405.
.. [3] Miettinen, Olli, and Markku Nurminen. "Comparative analysis of two
rates." Statistics in medicine 4, no. 2 (1985): 213-226.
.. [4] Newcombe, Robert G. 1998. “Interval Estimation for the Difference
between Independent Proportions: Comparison of Eleven Methods.”
Statistics in Medicine 17 (8): 873–90.
https://doi.org/10.1002/(SICI)1097-0258(19980430)17:8<873::AID-
SIM779>3.0.CO;2-I.
.. [5] Newcombe, Robert G., and Markku M. Nurminen. 2011. “In Defence of
Score Intervals for Proportions and Their Differences.” Communications
in Statistics - Theory and Methods 40 (7): 1271–82.
https://doi.org/10.1080/03610920903576580.
"""
method_default = {'diff': 'newcomb',
'ratio': 'log-adjusted',
'odds-ratio': 'logit-adjusted'}
# normalize compare name
if compare.lower() == 'or':
compare = 'odds-ratio'
if method is None:
method = method_default[compare]
method = method.lower()
if method.startswith('agr'):
method = 'agresti-caffo'
p1 = count1 / nobs1
p2 = count2 / nobs2
diff = p1 - p2
addone = 1 if method == 'agresti-caffo' else 0
if compare == 'diff':
if method in ['wald', 'agresti-caffo']:
count1_, nobs1_ = count1 + addone, nobs1 + 2 * addone
count2_, nobs2_ = count2 + addone, nobs2 + 2 * addone
p1_ = count1_ / nobs1_
p2_ = count2_ / nobs2_
diff_ = p1_ - p2_
var = p1_ * (1 - p1_) / nobs1_ + p2_ * (1 - p2_) / nobs2_
z = stats.norm.isf(alpha / 2)
d_wald = z * np.sqrt(var)
low = diff_ - d_wald
upp = diff_ + d_wald
elif method.startswith('newcomb'):
low1, upp1 = proportion_confint(count1, nobs1,
method='wilson', alpha=alpha)
low2, upp2 = proportion_confint(count2, nobs2,
method='wilson', alpha=alpha)
d_low = np.sqrt((p1 - low1)**2 + (upp2 - p2)**2)
d_upp = np.sqrt((p2 - low2)**2 + (upp1 - p1)**2)
low = diff - d_low
upp = diff + d_upp
elif method == "score":
low, upp = _score_confint_inversion(count1, nobs1, count2, nobs2,
compare=compare, alpha=alpha,
correction=correction)
else:
raise ValueError('method not recognized')
elif compare == 'ratio':
# ratio = p1 / p2
if method in ['log', 'log-adjusted']:
addhalf = 0.5 if method == 'log-adjusted' else 0
count1_, nobs1_ = count1 + addhalf, nobs1 + addhalf
count2_, nobs2_ = count2 + addhalf, nobs2 + addhalf
p1_ = count1_ / nobs1_
p2_ = count2_ / nobs2_
ratio_ = p1_ / p2_
var = (1 / count1_) - 1 / nobs1_ + 1 / count2_ - 1 / nobs2_
z = stats.norm.isf(alpha / 2)
d_log = z * np.sqrt(var)
low = np.exp(np.log(ratio_) - d_log)
upp = np.exp(np.log(ratio_) + d_log)
elif method == 'score':
res = _confint_riskratio_koopman(count1, nobs1, count2, nobs2,
alpha=alpha,
correction=correction)
low, upp = res.confint
else:
raise ValueError('method not recognized')
elif compare == 'odds-ratio':
# odds_ratio = p1 / (1 - p1) / p2 * (1 - p2)
if method in ['logit', 'logit-adjusted', 'logit-smoothed']:
if method in ['logit-smoothed']:
adjusted = _shrink_prob(count1, nobs1, count2, nobs2,
shrink_factor=2, return_corr=False)[0]
count1_, nobs1_, count2_, nobs2_ = adjusted
else:
addhalf = 0.5 if method == 'logit-adjusted' else 0
count1_, nobs1_ = count1 + addhalf, nobs1 + 2 * addhalf
count2_, nobs2_ = count2 + addhalf, nobs2 + 2 * addhalf
p1_ = count1_ / nobs1_
p2_ = count2_ / nobs2_
odds_ratio_ = p1_ / (1 - p1_) / p2_ * (1 - p2_)
var = (1 / count1_ + 1 / (nobs1_ - count1_) +
1 / count2_ + 1 / (nobs2_ - count2_))
z = stats.norm.isf(alpha / 2)
d_log = z * np.sqrt(var)
low = np.exp(np.log(odds_ratio_) - d_log)
upp = np.exp(np.log(odds_ratio_) + d_log)
elif method == "score":
low, upp = _score_confint_inversion(count1, nobs1, count2, nobs2,
compare=compare, alpha=alpha,
correction=correction)
else:
raise ValueError('method not recognized')
else:
raise ValueError('compare not recognized')
return low, upp
def _shrink_prob(count1, nobs1, count2, nobs2, shrink_factor=2,
return_corr=True):
"""
Shrink observed counts towards independence
Helper function for 'logit-smoothed' inference for the odds-ratio of two
independent proportions.
Parameters
----------
count1, nobs1 : float or int
count and sample size for first sample
count2, nobs2 : float or int
count and sample size for the second sample
shrink_factor : float
This corresponds to the number of observations that are added in total
proportional to the probabilities under independence.
return_corr : bool
If true, then only the correction term is returned
If false, then the corrected counts, i.e. original counts plus
correction term, are returned.
Returns
-------
count1_corr, nobs1_corr, count2_corr, nobs2_corr : float
correction or corrected counts
prob_indep :
TODO/Warning : this will change most likely
probabilities under independence, only returned if return_corr is
false.
"""
vectorized = any(np.size(i) > 1 for i in [count1, nobs1, count2, nobs2])
if vectorized:
raise ValueError("function is not vectorized")
nobs_col = np.array([count1 + count2, nobs1 - count1 + nobs2 - count2])
nobs_row = np.array([nobs1, nobs2])
nobs = nobs1 + nobs2
prob_indep = (nobs_col * nobs_row[:, None]) / nobs**2
corr = shrink_factor * prob_indep
if return_corr:
return (corr[0, 0], corr[0].sum(), corr[1, 0], corr[1].sum())
else:
return (count1 + corr[0, 0], nobs1 + corr[0].sum(),
count2 + corr[1, 0], nobs2 + corr[1].sum()), prob_indep
[docs]
def score_test_proportions_2indep(count1, nobs1, count2, nobs2, value=None,
compare='diff', alternative='two-sided',
correction=True, return_results=True):
"""
Score test for two independent proportions
This uses the constrained estimate of the proportions to compute
the variance under the Null hypothesis.
Parameters
----------
count1, nobs1 :
count and sample size for first sample
count2, nobs2 :
count and sample size for the second sample
value : float
diff, ratio or odds-ratio under the null hypothesis. If value is None,
then equality of proportions under the Null is assumed,
i.e. value=0 for 'diff' or value=1 for either rate or odds-ratio.
compare : string in ['diff', 'ratio' 'odds-ratio']
If compare is diff, then the confidence interval is for diff = p1 - p2.
If compare is ratio, then the confidence interval is for the risk ratio
defined by ratio = p1 / p2.
If compare is odds-ratio, then the confidence interval is for the
odds-ratio defined by or = p1 / (1 - p1) / (p2 / (1 - p2)
return_results : bool
If true, then a results instance with extra information is returned,
otherwise a tuple with statistic and pvalue is returned.
Returns
-------
results : results instance or tuple
If return_results is True, then a results instance with the
information in attributes is returned.
If return_results is False, then only ``statistic`` and ``pvalue``
are returned.
statistic : float
test statistic asymptotically normal distributed N(0, 1)
pvalue : float
p-value based on normal distribution
other attributes :
additional information about the hypothesis test
Notes
-----
Status: experimental, the type or extra information in the return might
change.
"""
value_default = 0 if compare == 'diff' else 1
if value is None:
# TODO: odds ratio does not work if value=1
value = value_default
nobs = nobs1 + nobs2
count = count1 + count2
p1 = count1 / nobs1
p2 = count2 / nobs2
if value == value_default:
# use pooled estimator if equality test
# shortcut, but required for odds ratio
prop0 = prop1 = count / nobs
# this uses index 0 from Miettinen Nurminned 1985
count0, nobs0 = count2, nobs2
p0 = p2
if compare == 'diff':
diff = value # hypothesis value
if diff != 0:
tmp3 = nobs
tmp2 = (nobs1 + 2 * nobs0) * diff - nobs - count
tmp1 = (count0 * diff - nobs - 2 * count0) * diff + count
tmp0 = count0 * diff * (1 - diff)
q = ((tmp2 / (3 * tmp3))**3 - tmp1 * tmp2 / (6 * tmp3**2) +
tmp0 / (2 * tmp3))
p = np.sign(q) * np.sqrt((tmp2 / (3 * tmp3))**2 -
tmp1 / (3 * tmp3))
a = (np.pi + np.arccos(q / p**3)) / 3
prop0 = 2 * p * np.cos(a) - tmp2 / (3 * tmp3)
prop1 = prop0 + diff
var = prop1 * (1 - prop1) / nobs1 + prop0 * (1 - prop0) / nobs0
if correction:
var *= nobs / (nobs - 1)
diff_stat = (p1 - p0 - diff)
elif compare == 'ratio':
# risk ratio
ratio = value
if ratio != 1:
a = nobs * ratio
b = -(nobs1 * ratio + count1 + nobs2 + count0 * ratio)
c = count
prop0 = (-b - np.sqrt(b**2 - 4 * a * c)) / (2 * a)
prop1 = prop0 * ratio
var = (prop1 * (1 - prop1) / nobs1 +
ratio**2 * prop0 * (1 - prop0) / nobs0)
if correction:
var *= nobs / (nobs - 1)
# NCSS looks incorrect for var, but it is what should be reported
# diff_stat = (p1 / p0 - ratio) # NCSS/PASS
diff_stat = (p1 - ratio * p0) # Miettinen Nurminen
elif compare in ['or', 'odds-ratio']:
# odds ratio
oratio = value
if oratio != 1:
# Note the constraint estimator does not handle odds-ratio = 1
a = nobs0 * (oratio - 1)
b = nobs1 * oratio + nobs0 - count * (oratio - 1)
c = -count
prop0 = (-b + np.sqrt(b**2 - 4 * a * c)) / (2 * a)
prop1 = prop0 * oratio / (1 + prop0 * (oratio - 1))
# try to avoid 0 and 1 proportions,
# those raise Zero Division Runtime Warnings
eps = 1e-10
prop0 = np.clip(prop0, eps, 1 - eps)
prop1 = np.clip(prop1, eps, 1 - eps)
var = (1 / (prop1 * (1 - prop1) * nobs1) +
1 / (prop0 * (1 - prop0) * nobs0))
if correction:
var *= nobs / (nobs - 1)
diff_stat = ((p1 - prop1) / (prop1 * (1 - prop1)) -
(p0 - prop0) / (prop0 * (1 - prop0)))
statistic, pvalue = _zstat_generic2(diff_stat, np.sqrt(var),
alternative=alternative)
if return_results:
res = HolderTuple(statistic=statistic,
pvalue=pvalue,
compare=compare,
method='score',
variance=var,
alternative=alternative,
prop1_null=prop1,
prop2_null=prop0,
)
return res
else:
return statistic, pvalue
[docs]
def test_proportions_2indep(count1, nobs1, count2, nobs2, value=None,
method=None, compare='diff',
alternative='two-sided', correction=True,
return_results=True):
"""
Hypothesis test for comparing two independent proportions
This assumes that we have two independent binomial samples.
The Null and alternative hypothesis are
for compare = 'diff'
- H0: prop1 - prop2 - value = 0
- H1: prop1 - prop2 - value != 0 if alternative = 'two-sided'
- H1: prop1 - prop2 - value > 0 if alternative = 'larger'
- H1: prop1 - prop2 - value < 0 if alternative = 'smaller'
for compare = 'ratio'
- H0: prop1 / prop2 - value = 0
- H1: prop1 / prop2 - value != 0 if alternative = 'two-sided'
- H1: prop1 / prop2 - value > 0 if alternative = 'larger'
- H1: prop1 / prop2 - value < 0 if alternative = 'smaller'
for compare = 'odds-ratio'
- H0: or - value = 0
- H1: or - value != 0 if alternative = 'two-sided'
- H1: or - value > 0 if alternative = 'larger'
- H1: or - value < 0 if alternative = 'smaller'
where odds-ratio or = prop1 / (1 - prop1) / (prop2 / (1 - prop2))
Parameters
----------
count1 : int
Count for first sample.
nobs1 : int
Sample size for first sample.
count2 : int
Count for the second sample.
nobs2 : int
Sample size for the second sample.
value : float
Value of the difference, risk ratio or odds ratio of 2 independent
proportions under the null hypothesis.
Default is equal proportions, 0 for diff and 1 for risk-ratio and for
odds-ratio.
method : string
Method for computing the hypothesis test. If method is None, then a
default method is used. The default might change as more methods are
added.
diff:
- 'wald',
- 'agresti-caffo'
- 'score' if correction is True, then this uses the degrees of freedom
correction ``nobs / (nobs - 1)`` as in Miettinen Nurminen 1985
ratio:
- 'log': wald test using log transformation
- 'log-adjusted': wald test using log transformation,
adds 0.5 to counts
- 'score': if correction is True, then this uses the degrees of freedom
correction ``nobs / (nobs - 1)`` as in Miettinen Nurminen 1985
odds-ratio:
- 'logit': wald test using logit transformation
- 'logit-adjusted': wald test using logit transformation,
adds 0.5 to counts
- 'logit-smoothed': wald test using logit transformation, biases
cell counts towards independence by adding two observations in
total.
- 'score' if correction is True, then this uses the degrees of freedom
correction ``nobs / (nobs - 1)`` as in Miettinen Nurminen 1985
compare : {'diff', 'ratio' 'odds-ratio'}
If compare is `diff`, then the hypothesis test is for the risk
difference diff = p1 - p2.
If compare is `ratio`, then the hypothesis test is for the
risk ratio defined by ratio = p1 / p2.
If compare is `odds-ratio`, then the hypothesis test is for the
odds-ratio defined by or = p1 / (1 - p1) / (p2 / (1 - p2)
alternative : {'two-sided', 'smaller', 'larger'}
alternative hypothesis, which can be two-sided or either one of the
one-sided tests.
correction : bool
If correction is True (default), then the Miettinen and Nurminen
small sample correction to the variance nobs / (nobs - 1) is used.
Applies only if method='score'.
return_results : bool
If true, then a results instance with extra information is returned,
otherwise a tuple with statistic and pvalue is returned.
Returns
-------
results : results instance or tuple
If return_results is True, then a results instance with the
information in attributes is returned.
If return_results is False, then only ``statistic`` and ``pvalue``
are returned.
statistic : float
test statistic asymptotically normal distributed N(0, 1)
pvalue : float
p-value based on normal distribution
other attributes :
additional information about the hypothesis test
See Also
--------
tost_proportions_2indep
confint_proportions_2indep
Notes
-----
Status: experimental, API and defaults might still change.
More ``methods`` will be added.
The current default methods are
- 'diff': 'agresti-caffo',
- 'ratio': 'log-adjusted',
- 'odds-ratio': 'logit-adjusted'
"""
method_default = {'diff': 'agresti-caffo',
'ratio': 'log-adjusted',
'odds-ratio': 'logit-adjusted'}
# normalize compare name
if compare.lower() == 'or':
compare = 'odds-ratio'
if method is None:
method = method_default[compare]
method = method.lower()
if method.startswith('agr'):
method = 'agresti-caffo'
if value is None:
# TODO: odds ratio does not work if value=1 for score test
value = 0 if compare == 'diff' else 1
count1, nobs1, count2, nobs2 = map(np.asarray,
[count1, nobs1, count2, nobs2])
p1 = count1 / nobs1
p2 = count2 / nobs2
diff = p1 - p2
ratio = p1 / p2
odds_ratio = p1 / (1 - p1) / p2 * (1 - p2)
res = None
if compare == 'diff':
if method in ['wald', 'agresti-caffo']:
addone = 1 if method == 'agresti-caffo' else 0
count1_, nobs1_ = count1 + addone, nobs1 + 2 * addone
count2_, nobs2_ = count2 + addone, nobs2 + 2 * addone
p1_ = count1_ / nobs1_
p2_ = count2_ / nobs2_
diff_stat = p1_ - p2_ - value
var = p1_ * (1 - p1_) / nobs1_ + p2_ * (1 - p2_) / nobs2_
statistic = diff_stat / np.sqrt(var)
distr = 'normal'
elif method.startswith('newcomb'):
msg = 'newcomb not available for hypothesis test'
raise NotImplementedError(msg)
elif method == 'score':
# Note score part is the same call for all compare
res = score_test_proportions_2indep(count1, nobs1, count2, nobs2,
value=value, compare=compare,
alternative=alternative,
correction=correction,
return_results=return_results)
if return_results is False:
statistic, pvalue = res[:2]
distr = 'normal'
# TODO/Note score_test_proportion_2samp returns statistic and
# not diff_stat
diff_stat = None
else:
raise ValueError('method not recognized')
elif compare == 'ratio':
if method in ['log', 'log-adjusted']:
addhalf = 0.5 if method == 'log-adjusted' else 0
count1_, nobs1_ = count1 + addhalf, nobs1 + addhalf
count2_, nobs2_ = count2 + addhalf, nobs2 + addhalf
p1_ = count1_ / nobs1_
p2_ = count2_ / nobs2_
ratio_ = p1_ / p2_
var = (1 / count1_) - 1 / nobs1_ + 1 / count2_ - 1 / nobs2_
diff_stat = np.log(ratio_) - np.log(value)
statistic = diff_stat / np.sqrt(var)
distr = 'normal'
elif method == 'score':
res = score_test_proportions_2indep(count1, nobs1, count2, nobs2,
value=value, compare=compare,
alternative=alternative,
correction=correction,
return_results=return_results)
if return_results is False:
statistic, pvalue = res[:2]
distr = 'normal'
diff_stat = None
else:
raise ValueError('method not recognized')
elif compare == "odds-ratio":
if method in ['logit', 'logit-adjusted', 'logit-smoothed']:
if method in ['logit-smoothed']:
adjusted = _shrink_prob(count1, nobs1, count2, nobs2,
shrink_factor=2, return_corr=False)[0]
count1_, nobs1_, count2_, nobs2_ = adjusted
else:
addhalf = 0.5 if method == 'logit-adjusted' else 0
count1_, nobs1_ = count1 + addhalf, nobs1 + 2 * addhalf
count2_, nobs2_ = count2 + addhalf, nobs2 + 2 * addhalf
p1_ = count1_ / nobs1_
p2_ = count2_ / nobs2_
odds_ratio_ = p1_ / (1 - p1_) / p2_ * (1 - p2_)
var = (1 / count1_ + 1 / (nobs1_ - count1_) +
1 / count2_ + 1 / (nobs2_ - count2_))
diff_stat = np.log(odds_ratio_) - np.log(value)
statistic = diff_stat / np.sqrt(var)
distr = 'normal'
elif method == 'score':
res = score_test_proportions_2indep(count1, nobs1, count2, nobs2,
value=value, compare=compare,
alternative=alternative,
correction=correction,
return_results=return_results)
if return_results is False:
statistic, pvalue = res[:2]
distr = 'normal'
diff_stat = None
else:
raise ValueError('method "%s" not recognized' % method)
else:
raise ValueError('compare "%s" not recognized' % compare)
if distr == 'normal' and diff_stat is not None:
statistic, pvalue = _zstat_generic2(diff_stat, np.sqrt(var),
alternative=alternative)
if return_results:
if res is None:
res = HolderTuple(statistic=statistic,
pvalue=pvalue,
compare=compare,
method=method,
diff=diff,
ratio=ratio,
odds_ratio=odds_ratio,
variance=var,
alternative=alternative,
value=value,
)
else:
# we already have a return result from score test
# add missing attributes
res.diff = diff
res.ratio = ratio
res.odds_ratio = odds_ratio
res.value = value
return res
else:
return statistic, pvalue
[docs]
def tost_proportions_2indep(count1, nobs1, count2, nobs2, low, upp,
method=None, compare='diff', correction=True):
"""
Equivalence test based on two one-sided `test_proportions_2indep`
This assumes that we have two independent binomial samples.
The Null and alternative hypothesis for equivalence testing are
for compare = 'diff'
- H0: prop1 - prop2 <= low or upp <= prop1 - prop2
- H1: low < prop1 - prop2 < upp
for compare = 'ratio'
- H0: prop1 / prop2 <= low or upp <= prop1 / prop2
- H1: low < prop1 / prop2 < upp
for compare = 'odds-ratio'
- H0: or <= low or upp <= or
- H1: low < or < upp
where odds-ratio or = prop1 / (1 - prop1) / (prop2 / (1 - prop2))
Parameters
----------
count1, nobs1 :
count and sample size for first sample
count2, nobs2 :
count and sample size for the second sample
low, upp :
equivalence margin for diff, risk ratio or odds ratio
method : string
method for computing the hypothesis test. If method is None, then a
default method is used. The default might change as more methods are
added.
diff:
- 'wald',
- 'agresti-caffo'
- 'score' if correction is True, then this uses the degrees of freedom
correction ``nobs / (nobs - 1)`` as in Miettinen Nurminen 1985.
ratio:
- 'log': wald test using log transformation
- 'log-adjusted': wald test using log transformation,
adds 0.5 to counts
- 'score' if correction is True, then this uses the degrees of freedom
correction ``nobs / (nobs - 1)`` as in Miettinen Nurminen 1985.
odds-ratio:
- 'logit': wald test using logit transformation
- 'logit-adjusted': : wald test using logit transformation,
adds 0.5 to counts
- 'logit-smoothed': : wald test using logit transformation, biases
cell counts towards independence by adding two observations in
total.
- 'score' if correction is True, then this uses the degrees of freedom
correction ``nobs / (nobs - 1)`` as in Miettinen Nurminen 1985
compare : string in ['diff', 'ratio' 'odds-ratio']
If compare is `diff`, then the hypothesis test is for
diff = p1 - p2.
If compare is `ratio`, then the hypothesis test is for the
risk ratio defined by ratio = p1 / p2.
If compare is `odds-ratio`, then the hypothesis test is for the
odds-ratio defined by or = p1 / (1 - p1) / (p2 / (1 - p2).
correction : bool
If correction is True (default), then the Miettinen and Nurminen
small sample correction to the variance nobs / (nobs - 1) is used.
Applies only if method='score'.
Returns
-------
pvalue : float
p-value is the max of the pvalues of the two one-sided tests
t1 : test results
results instance for one-sided hypothesis at the lower margin
t1 : test results
results instance for one-sided hypothesis at the upper margin
See Also
--------
test_proportions_2indep
confint_proportions_2indep
Notes
-----
Status: experimental, API and defaults might still change.
The TOST equivalence test delegates to `test_proportions_2indep` and has
the same method and comparison options.
"""
tt1 = test_proportions_2indep(count1, nobs1, count2, nobs2, value=low,
method=method, compare=compare,
alternative='larger',
correction=correction,
return_results=True)
tt2 = test_proportions_2indep(count1, nobs1, count2, nobs2, value=upp,
method=method, compare=compare,
alternative='smaller',
correction=correction,
return_results=True)
# idx_max = 1 if t1.pvalue < t2.pvalue else 0
idx_max = np.asarray(tt1.pvalue < tt2.pvalue, int)
statistic = np.choose(idx_max, [tt1.statistic, tt2.statistic])
pvalue = np.choose(idx_max, [tt1.pvalue, tt2.pvalue])
res = HolderTuple(statistic=statistic,
pvalue=pvalue,
compare=compare,
method=method,
results_larger=tt1,
results_smaller=tt2,
title="Equivalence test for 2 independent proportions"
)
return res
def _std_2prop_power(diff, p2, ratio=1, alpha=0.05, value=0):
"""
Compute standard error under null and alternative for 2 proportions
helper function for power and sample size computation
"""
if value != 0:
msg = 'non-zero diff under null, value, is not yet implemented'
raise NotImplementedError(msg)
nobs_ratio = ratio
p1 = p2 + diff
# The following contains currently redundant variables that will
# be useful for different options for the null variance
p_pooled = (p1 + p2 * ratio) / (1 + ratio)
# probabilities for the variance for the null statistic
p1_vnull, p2_vnull = p_pooled, p_pooled
p2_alt = p2
p1_alt = p2_alt + diff
std_null = _std_diff_prop(p1_vnull, p2_vnull, ratio=nobs_ratio)
std_alt = _std_diff_prop(p1_alt, p2_alt, ratio=nobs_ratio)
return p_pooled, std_null, std_alt
[docs]
def power_proportions_2indep(diff, prop2, nobs1, ratio=1, alpha=0.05,
value=0, alternative='two-sided',
return_results=True):
"""
Power for ztest that two independent proportions are equal
This assumes that the variance is based on the pooled proportion
under the null and the non-pooled variance under the alternative
Parameters
----------
diff : float
difference between proportion 1 and 2 under the alternative
prop2 : float
proportion for the reference case, prop2, proportions for the
first case will be computed using p2 and diff
p1 = p2 + diff
nobs1 : float or int
number of observations in sample 1
ratio : float
sample size ratio, nobs2 = ratio * nobs1
alpha : float in interval (0,1)
Significance level, e.g. 0.05, is the probability of a type I
error, that is wrong rejections if the Null Hypothesis is true.
value : float
currently only `value=0`, i.e. equality testing, is supported
alternative : string, 'two-sided' (default), 'larger', 'smaller'
Alternative hypothesis whether the power is calculated for a
two-sided (default) or one sided test. The one-sided test can be
either 'larger', 'smaller'.
return_results : bool
If true, then a results instance with extra information is returned,
otherwise only the computed power is returned.
Returns
-------
results : results instance or float
If return_results is True, then a results instance with the
information in attributes is returned.
If return_results is False, then only the power is returned.
power : float
Power of the test, e.g. 0.8, is one minus the probability of a
type II error. Power is the probability that the test correctly
rejects the Null Hypothesis if the Alternative Hypothesis is true.
Other attributes in results instance include :
p_pooled
pooled proportion, used for std_null
std_null
standard error of difference under the null hypothesis (without
sqrt(nobs1))
std_alt
standard error of difference under the alternative hypothesis
(without sqrt(nobs1))
"""
# TODO: avoid possible circular import, check if needed
from statsmodels.stats.power import normal_power_het
p_pooled, std_null, std_alt = _std_2prop_power(diff, prop2, ratio=ratio,
alpha=alpha, value=value)
pow_ = normal_power_het(diff, nobs1, alpha, std_null=std_null,
std_alternative=std_alt,
alternative=alternative)
if return_results:
res = Holder(power=pow_,
p_pooled=p_pooled,
std_null=std_null,
std_alt=std_alt,
nobs1=nobs1,
nobs2=ratio * nobs1,
nobs_ratio=ratio,
alpha=alpha,
)
return res
else:
return pow_
[docs]
def samplesize_proportions_2indep_onetail(diff, prop2, power, ratio=1,
alpha=0.05, value=0,
alternative='two-sided'):
"""
Required sample size assuming normal distribution based on one tail
This uses an explicit computation for the sample size that is required
to achieve a given power corresponding to the appropriate tails of the
normal distribution. This ignores the far tail in a two-sided test
which is negligible in the common case when alternative and null are
far apart.
Parameters
----------
diff : float
Difference between proportion 1 and 2 under the alternative
prop2 : float
proportion for the reference case, prop2, proportions for the
first case will be computing using p2 and diff
p1 = p2 + diff
power : float
Power for which sample size is computed.
ratio : float
Sample size ratio, nobs2 = ratio * nobs1
alpha : float in interval (0,1)
Significance level, e.g. 0.05, is the probability of a type I
error, that is wrong rejections if the Null Hypothesis is true.
value : float
Currently only `value=0`, i.e. equality testing, is supported
alternative : string, 'two-sided' (default), 'larger', 'smaller'
Alternative hypothesis whether the power is calculated for a
two-sided (default) or one sided test. In the case of a one-sided
alternative, it is assumed that the test is in the appropriate tail.
Returns
-------
nobs1 : float
Number of observations in sample 1.
"""
# TODO: avoid possible circular import, check if needed
from statsmodels.stats.power import normal_sample_size_one_tail
if alternative in ['two-sided', '2s']:
alpha = alpha / 2
_, std_null, std_alt = _std_2prop_power(diff, prop2, ratio=ratio,
alpha=alpha, value=value)
nobs = normal_sample_size_one_tail(diff, power, alpha, std_null=std_null,
std_alternative=std_alt)
return nobs
[docs]
def _score_confint_inversion(count1, nobs1, count2, nobs2, compare='diff',
alpha=0.05, correction=True):
"""
Compute score confidence interval by inverting score test
Parameters
----------
count1, nobs1 :
Count and sample size for first sample.
count2, nobs2 :
Count and sample size for the second sample.
compare : string in ['diff', 'ratio' 'odds-ratio']
If compare is `diff`, then the confidence interval is for
diff = p1 - p2.
If compare is `ratio`, then the confidence interval is for the
risk ratio defined by ratio = p1 / p2.
If compare is `odds-ratio`, then the confidence interval is for the
odds-ratio defined by or = p1 / (1 - p1) / (p2 / (1 - p2).
alpha : float in interval (0,1)
Significance level, e.g. 0.05, is the probability of a type I
error, that is wrong rejections if the Null Hypothesis is true.
correction : bool
If correction is True (default), then the Miettinen and Nurminen
small sample correction to the variance nobs / (nobs - 1) is used.
Applies only if method='score'.
Returns
-------
low : float
Lower confidence bound.
upp : float
Upper confidence bound.
"""
def func(v):
r = test_proportions_2indep(count1, nobs1, count2, nobs2,
value=v, compare=compare, method='score',
correction=correction,
alternative="two-sided")
return r.pvalue - alpha
rt0 = test_proportions_2indep(count1, nobs1, count2, nobs2,
value=0, compare=compare, method='score',
correction=correction,
alternative="two-sided")
# use default method to get starting values
# this will not work if score confint becomes default
# maybe use "wald" as alias that works for all compare statistics
use_method = {"diff": "wald", "ratio": "log", "odds-ratio": "logit"}
rci0 = confint_proportions_2indep(count1, nobs1, count2, nobs2,
method=use_method[compare],
compare=compare, alpha=alpha)
# Note diff might be negative
ub = rci0[1] + np.abs(rci0[1]) * 0.5
lb = rci0[0] - np.abs(rci0[0]) * 0.25
if compare == 'diff':
param = rt0.diff
# 1 might not be the correct upper bound because
# rootfinding is for the `diff` and not for a probability.
ub = min(ub, 0.99999)
elif compare == 'ratio':
param = rt0.ratio
ub *= 2 # add more buffer
if compare == 'odds-ratio':
param = rt0.odds_ratio
# root finding for confint bounds
upp = optimize.brentq(func, param, ub)
low = optimize.brentq(func, lb, param)
return low, upp
def _confint_riskratio_koopman(count1, nobs1, count2, nobs2, alpha=0.05,
correction=True):
"""
Score confidence interval for ratio or proportions, Koopman/Nam
signature not consistent with other functions
When correction is True, then the small sample correction nobs / (nobs - 1)
by Miettinen/Nurminen is used.
"""
# The names below follow Nam
x0, x1, n0, n1 = count2, count1, nobs2, nobs1
x = x0 + x1
n = n0 + n1
z = stats.norm.isf(alpha / 2)**2
if correction:
# Mietinnen/Nurminen small sample correction
z *= n / (n - 1)
# z = stats.chi2.isf(alpha, 1)
# equ 6 in Nam 1995
a1 = n0 * (n0 * n * x1 + n1 * (n0 + x1) * z)
a2 = - n0 * (n0 * n1 * x + 2 * n * x0 * x1 + n1 * (n0 + x0 + 2 * x1) * z)
a3 = 2 * n0 * n1 * x0 * x + n * x0 * x0 * x1 + n0 * n1 * x * z
a4 = - n1 * x0 * x0 * x
p_roots_ = np.sort(np.roots([a1, a2, a3, a4]))
p_roots = p_roots_[:2][::-1]
# equ 5
ci = (1 - (n1 - x1) * (1 - p_roots) / (x0 + n1 - n * p_roots)) / p_roots
res = Holder()
res.confint = ci
res._p_roots = p_roots_ # for unit tests, can be dropped
return res
def _confint_riskratio_paired_nam(table, alpha=0.05):
"""
Confidence interval for marginal risk ratio for matched pairs
need full table
success fail marginal
success x11 x10 x1.
fail x01 x00 x0.
marginal x.1 x.0 n
The confidence interval is for the ratio p1 / p0 where
p1 = x1. / n and
p0 - x.1 / n
Todo: rename p1 to pa and p2 to pb, so we have a, b for treatment and
0, 1 for success/failure
current namings follow Nam 2009
status
testing:
compared to example in Nam 2009
internal polynomial coefficients in calculation correspond at around
4 decimals
confidence interval agrees only at 2 decimals
"""
x11, x10, x01, x00 = np.ravel(table)
n = np.sum(table) # nobs
p10, p01 = x10 / n, x01 / n
p1 = (x11 + x10) / n
p0 = (x11 + x01) / n
q00 = 1 - x00 / n
z2 = stats.norm.isf(alpha / 2)**2
# z = stats.chi2.isf(alpha, 1)
# before equ 3 in Nam 2009
g1 = (n * p0 + z2 / 2) * p0
g2 = - (2 * n * p1 * p0 + z2 * q00)
g3 = (n * p1 + z2 / 2) * p1
a0 = g1**2 - (z2 * p0 / 2)**2
a1 = 2 * g1 * g2
a2 = g2**2 + 2 * g1 * g3 + z2**2 * (p1 * p0 - 2 * p10 * p01) / 2
a3 = 2 * g2 * g3
a4 = g3**2 - (z2 * p1 / 2)**2
p_roots = np.sort(np.roots([a0, a1, a2, a3, a4]))
# p_roots = np.sort(np.roots([1, a1 / a0, a2 / a0, a3 / a0, a4 / a0]))
ci = [p_roots.min(), p_roots.max()]
res = Holder()
res.confint = ci
res.p = p1, p0
res._p_roots = p_roots # for unit tests, can be dropped
return res
Last update:
Apr 19, 2024