# Example 10.8: The upper 95% CI is 3.69

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Today: what can you say about the probability of an event if the observed number of events is 0? It turns out that the upper 95% CI for the probability is 3.69/N. There’s a sweet little paper with some rationale for this, but it’s in my other office. And I couldn’t recall the precise value– so I used SAS and R to demonstrate it to myself.

**R**

The R code is remarkably concise. After generating some Ns, we write a little function to perform the test and extract the (exact) upper 95% confidence limit. This is facilitated by the “…” notation, which passes along unused arguments to functions. Then we use

`apply()`to call the new function for each N, passing the numerator 0 each time. Note that

`apply()`needs a matrix argument, so the simple vector of Ns is converted to a matrix before use. [The

`sapply()`function will accept a vector input, but took about 8 times as long to run.] Finally, we plot the upper limit * N against N. showing the asymptote. A log scaled x-axis is useful here, and is achieved with the

`log='x'`option. (Section 5.3.12.) the result is shown above.

bin.m = seq(10, 10000, by=5) mybt = function(...) { binom.test(...)$conf.int[2] } uci = apply(as.matrix(bin.m), 1, mybt, x=0) plot(y=bin.m * uci, x=bin.m, ylim=c(0,4), type="l", lwd=5, col="red", cex=5, log='x', ylab="Exact upper CI", xlab="Sample size", main="Upper CI when there are 0 cases observed") abline(h=3.69)

**SAS**

In SAS, the data, really just the N and a numerator of 0, are generated in a

`data`step. The CI are found using the

`binomial`option in the

`proc freq tables`statement and saved using the

`output`statement. Note that the

`weight`statement is used here to avoid having a row for each Bernoulli trial.

data binm; do n = 10 to 10000 by 5; x=0; output; end; run; ods select none; proc freq data=binm; by n; weight n; tables x / binomial; output out=bp binomial; run; ods select all;To calculate the upper limit*N, another

`data`step is needed– note that in this setting SAS will only produce the lower limit against the probability that all observations share the same value, thus the subtraction from 1 shown below. The log scale x-axis is obtained with the

`logbase`option to the

`axis`statement. (Section 5.3.12.) The result is shown below.

data uci; set bp; limit = (1-xl_bin) * n; run; axis1 order = (0 to 4 by 1); axis2 logbase=10 logstyle=expand; symbol1 i = j v = none c = red w=5 l=1; proc gplot data=uci; plot limit * n / vref=3.69 vaxis=axis1 haxis=axis2; label n="Sample size" limit="Exact upper CI"; run; quit;It’s clear that the upper 95% limit on the number of successes asymptotes to about 3.69. Thus the upper limit on the binomial probability p is 3.69/N.

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