# Finite-population SPRT for Population Percentage¶

## Example of the SPRT without replacement¶

This notebook develops a sequential probability ratio test for the fraction of items labeled "1" in a population of $N$ items of which $Np$ are labeled $1$ and $N(1-p)$ are labeled "0."

This is a special case of the result derived in the notebook Wald's Sequential Probability Ratio Test.

There is a population of $N$ items. Item $j$ has "value" $a_j \in \{0, 1\}$.

Define $p = \frac{1}{N}\sum_j a_j$ to be the population percentage.

We want to test the hypothesis $H_0$ that $p = p_0$ against the alternative hypothesis $H_1$ that $p = p_1$, for some fixed $p_1 > p_0$.

We will draw items sequentially, without replacement, such that the chance that item $i$ is selected in draw $j$, assuming it has not been selected already, is $1/(N-j+1)$. Let ${\mathcal B_{j-1}}$ be the indices of the items selected up to and including the $j-1$st draw, and ${\mathcal B_0} \equiv \emptyset$.

Let $\mathbb B_j$ denote the index of the item selected at random in the $j$th draw.

The chance that the first draw ${\mathbb B_1}$ gives an item with value 1, i.e., $\Pr \{a_{\mathbb B_1} = 1\}$, is $\frac{1}{N}\sum_b a_b$. Under $H_0$, this chance is $p_{01} = p_0$; under $H_1$, this chance is $p_{11} = p_1$.

Given the values of $\{a_{\mathbb B_k}\}_{k=1}^i$, the conditional probability that the $i$th draw gives an item with value 1 is

$$\Pr \{a_{\mathbb B_i} = 1 | {\mathcal B_{i-1}} \} = \frac{ \sum_{b \notin {\mathcal B_{i-1}}} a_b}{N-i+1}.$$

Under $H_0$, this chance is

$$p_{0i} = \frac{Np_0 - \sum_{b \in {\mathcal B_{i-1}}} a_b}{N - i + 1}.$$

Under $H_1$, this chance is

$$p_{1i} = \frac{Np_1 - \sum_{b \in {\mathcal B_{i-1}}} a_b}{N - i + 1}.$$

Let $X_i$ be the indicator of the event that the $i$th draw gives an item with value $1$, i.e., the indicator of the event $a_{\mathbb B_i} = 1$. The likelihood ratio for a given sequence $\{X_k\}_{k=1}^i$ is

$$\mbox{LR} = \frac{\prod_{k=1}^i p_{1k}^{X_k}(1-p_{1k})^{1-X_k}} {\prod_{k=1}^i p_{0k}^{X_k}(1-p_{0k})^{1-X_k}}.$$

This can be simplified: $p_{0k}$ and $p_{1k}$ have the same denominator, $N - k + 1$, and their numerators share a term. Define $A(k) \equiv \sum_{b \in {\mathcal B_{k-1}}}$. Then

$$\mbox{LR} = \prod_{k=1}^i \left ( \frac{Np_1 - A(k)}{Np_0 - A(k)} \right )^{X_k} \left ( \frac{N(1-p_1) - (k-1-A(k))}{N(1-p_0) - (k-1 - A(k))} \right )^{1-X_k}.$$

where the products are defined to be infinite if any denominator vanishes (or is negative).

If $H_0$ is true, the chance that $\mbox{LR}$ is ever greater than $1/\alpha$ is at most $\alpha$.

In [1]:
# This is the first cell with code: set up the Python environment
%matplotlib inline
from __future__ import division, print_function
import matplotlib.pyplot as plt
import math
import numpy as np
import numpy.random
import scipy as sp
import scipy.stats
# For interactive widgets
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
from IPython.display import clear_output, display, HTML
In [2]:
np.random.seed(1234567890) # set seed for reproducibility
In [3]:
def LRFromTrials(trials, N, p0, p1):
'''
Finds the sequence of likelihood ratios for the hypothesis that the population
percentage is p1 to the hypothesis that it is p0, for sampling without replacement
from a population of size N.
'''
A = np.cumsum(np.insert(trials, 0, 0)) # so that cumsum does the right thing
terms = np.ones(N)
for k in range(len(trials)):
if trials[k] == 1.0:
if (N*p0 - A[k]) > 0:
terms[k] = np.max([N*p1 - A[k], 0])/(N*p0 - A[k])
else:
terms[k] = np.inf
else:
if (N*(1-p0) - k + 1 + A[k]) > 0:
terms[k] = np.max([(N*(1-p1) - k + 1 + A[k]), 0])/(N*(1-p0) - k + 1 + A[k])
else:
terms[k] = np.inf
return(np.cumprod(terms))
In [8]:
def plotBernoulliSPRT(N, p, p0, p1, alpha):
'''
Plots the progress of a one-sided SPRT for N dependent Bernoulli trials
in sampling without replacement from a population of size N with a
fraction p of items labeled "1," for testing the hypothesis that p=p0
against the hypothesis p=p1 at significance level alpha
'''
plt.clf()
fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)
trials = np.zeros(N)
nOnes = int(math.floor(N*p))
trials[0:nOnes] = np.ones(nOnes)
np.random.shuffle(trials) # items are in random order

LRs = np.nan_to_num(LRFromTrials(trials, N, p0, p1))
logLRs = np.nan_to_num(np.log(LRs))

LRs[LRs > 10**6] = 10**6 # avoid plot overflow
logLRs[logLRs > 10**6] = 10**6 # avoid plot overflow

#
ax[0].plot(range(N),LRs, color='b')
ax[0].axhline(y=1/alpha, xmin=0, xmax=N, color='g', label=r'$1/\alpha$')
ax[0].set_title('Simulation of Wald\'s SPRT for population percentage, w/o replacement')
ax[0].set_ylabel('LR')
ax[0].legend(loc='best')
#
ax[1].plot(range(N),logLRs, color='b', linestyle='--')
ax[1].axhline(y=math.log(1/alpha), xmin=0, xmax=N, color='g', label=r'$log(1/\alpha)$')
ax[1].set_ylabel('log(LR)')
ax[1].set_xlabel('trials')
ax[1].legend(loc='best')
plt.show()

interact(plotBernoulliSPRT,\
N=widgets.IntSlider(min=500, max=50000, step=500, value=5000),\
p=widgets.FloatSlider(min=0.001, max=1, step=0.01, value=.51),\
p0=widgets.FloatSlider(min=0.001, max=1, step=0.01, value=.5),\
p1=widgets.FloatSlider(min=0.001, max=1, step=0.01, value=.51),\
alpha=widgets.FloatSlider(min=0.001, max=1, step=0.01, value=.05)
)
Out[8]:
<function __main__.plotBernoulliSPRT>

## Simulate the distribution of sample sizes needed to reject¶

In [5]:
alpha = 0.05                   # significance level
reps = int(10**4)              # number of replications
p, p0, p1 = [0.525, 0.5, 0.525]  # need p > p0 or might never reject
N = 10000                       # population size
dist = np.zeros(reps)          # allocate space for the results

trials = np.zeros(N)
nOnes = int(math.floor(N*p))
trials[0:nOnes] = np.ones(nOnes) # trials now contains math.floor(n*p) ones

for i in np.arange(reps):
np.random.shuffle(trials) # items are in random order
LRs = LRFromTrials(trials, N, p0, p1) # likelihood ratios for this realization
dist[i] = np.min(np.where(LRs >= 1/alpha)) # trials at which threshold is crossed
In [6]:
# report mean, median, and 90th percentile
print(np.mean(dist), np.percentile(dist, [50, 90]))
1784.1438 [ 1526.  3265.]