I want to generate a sample of random variates choosing 5 out of 7 days of the week, $X_1,\ldots,X_5$, such that the aggregate counts resemble a given day-of-week profile, namely $$\mathbb{E}[\mathbb{I}_i(X_1)+\cdots+\mathbb{I}_i(X_5)]=5p_i$$ for $i=1,\ldots,7$.

I am using the traditional inverse transform method with pseudo-random uniform U numbers and the desired cumulative distribution. Why is the sample distribution flatter than the generating distribution?

I am assuming it has to do somehow with conditional probabilities. Assuming the pseudo-random numbers are acceptable, what is the best way to accomplish this?

Below stands my implementation in R:

counts <- c(2268, 3232, 4173, 4113, 4079, 3877, 2648)
dow.pdf <- counts / sum(counts)
dow.cdf <- cumsum(dow.pdf)

randDow <- function(n) {
  dow <- NULL
  while (length(dow) < n) {
    #generate pseudo-random number
    U <- runif(1, min = 0, max = 1)

    #find the position in the CDF
    rv <- which.min(U > dow.cdf)

    #check for duplicates
    if (!(rv %in% dow)) {
      dow <- c(dow, rv)

#generate 10000 random variates method 1
s1 <- t(replicate(10000,c(sort(sample(1:7,5,rep=F,prob=dow.pdf)))))

#generate 10000 random variates method 2
s2 <- t(replicate(10000, randDow(5)))

#summarize sample results
ps1 <- table(s1)/sum(table(s1))
ps2 <- table(s2)/sum(table(s2))

#compare visually
barplot(matrix(c(dow.pdf, ps1, ps2), nr=3, byrow=TRUE), beside=T, 
    names.arg=1:7, main="Day of Week Distribution")
legend("topleft", c("pdf","ps1", "ps2"), pch=15, cex=0.7,
  • 2
    $\begingroup$ Can you clarify what "I want to generate a sample of random variates choosing 5 out of 7 days of week such that the aggregate counts resemble a given day-of-week profile" means? $\endgroup$
    – dsaxton
    Feb 23 '16 at 21:01
  • $\begingroup$ Means when counting the number of times each day of week appears in the sample, the proportions with respect to the total match the desired proportions. $\endgroup$
    – arameri
    Feb 23 '16 at 21:13

Update [02/30]: As my colleague Robin Ryder pointed out to me, the problem is solved in Brewer's book Sampling with unequal probabilities. This resolution is actually discussed in an earlier Cross validated question. It is however unclear to me whether or not there exists a sampling strategy with fixed probabilities, in the sense of running


to produce the sample with expected frequencies $p_i$...

The traditional inverse cdf method works for a single simulation. However, if you generate 5 draws without replication, according to the frequencies given by counts, the marginal distribution of those draws is no longer the empirical cdf dow.cdf. The fact that both your methods lead to highly similar frequencies is reflecting upon this difference.

For instance, consider the formal case when counts=c(1000,100,1,1)) and you draw n=2 out of 4 without replacement: because of the huge imbalance between the first two counts and the last two counts, most draws by sample((1:4),2,rep=FALSE,prob=c(1e3,1e2,1,1)) will be 1 2 despite 2 having a probability ten times smaller. As shown by the picture below:enter image description here

However, if the number of draws is reasonably small against the number of possible values and if the probabilities are not too different, there may exist a solution. For instance, if one generates only 2 draws out of 7 possible values, and if one wants to achieve the probability vector $\mathbf{q}$ on the resulting sample, the sampling probability vector $\mathbf{p}$ is solution of the equations $(i=1,\ldots,7)$ $$ 2q_i=p_i+\sum_{j\ne i} p_j\,\frac{p_i}{1-p_j} $$ While solving this equation in a closed form is not feasible, a recursion based on the fact that the uniform probability is a fixed point may produce a solution. Here is an example where it works:

#function to be inverted
#component in the above
#target probability
#probability when resampling 
while (sum(abs(2*kuh-targ(peh)))>.001){

Running this code leads to

> peh
[1] 0.03261515 0.06597545 0.10094319 0.13761820 0.17673115 0.21921258 0.26690427

and checking that it works:

> s1 <- t(replicate(10000,c(sort(sample(1:7,2,rep=F,prob=peh)))))
> table(s1)/sum(table(s1))
      1       2       3       4       5       6       7 
0.03710 0.07225 0.10695 0.13990 0.17630 0.21545 0.25205 
> kuh
[1] 0.03571429 0.07142857 0.10714286 0.14285714 0.17857143 0.21428571 0.25000000

But if one picks a more extreme repartition of the draws, like


there is no solution (found by this recursive approach). Extending to more draws is equally feasible if a wee bit more cumbersome. For instance, here is the version for 3 draws:

  for (i in 1:7){

which leads to a solution using the iterative pluggin approach as follows:

while (max(abs(3*kuh-targ(peh)))>.001){

as it stops at

> peh
[1] 0.02797048 0.05788001 0.09031821 0.12651015 0.16894929 0.22434742 0.30402444

which is close enough to the target:

> max(abs(3*kuh-targ(peh)))
[1] 0.0004822188
  • $\begingroup$ Thanks Xi'an. I understand your point. The sample() function in R already adjusts the probabilities when the sampling is without replacement (according to the documentation). My custom function is trying to do the same numerically. I am not concerned about the difference of the two sampling methods. What I am concerned is about the difference between the desired counts distribution and the resulting counts distribution. $\endgroup$
    – arameri
    Feb 23 '16 at 21:56
  • $\begingroup$ Yes, the resulting imbalance is the problem. How to overcome that imbalance is the exact question still unanswered. I have modified the counts by trial-an-error to achieve approximate balance. However, I believe there must be some theoretical way to calculate. $\endgroup$
    – arameri
    Feb 24 '16 at 22:54

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.