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As question, I have found something similar here, but how to do it in R?


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Is the example at stats.stackexchange.com/q/5206/919 helpful? –  whuber May 18 '11 at 5:06
poisson.test gives identical answers to the page that you pointed to for count data. –  deinst Dec 23 '11 at 13:44

2 Answers 2

up vote 5 down vote accepted

You are looking for a confidence interval around the count from a Poisson process. If you put for example 42 into your linked example you get

You observed 42 objects in a certain volume or 42 events in a certain time period.

Exact Poisson confidence interval:

  • The 90% confidence interval extends from 31.94 to 54.32
  • The 95% confidence interval extends from 30.27 to 56.77
  • The 99% confidence interval extends from 27.18 to 61.76

You can get this in R using poisson.test. For example

> poisson.test(42, conf.level = 0.9 )

        Exact Poisson test

data:  42 time base: 1 
number of events = 42, time base = 1, p-value < 2.2e-16
alternative hypothesis: true event rate is not equal to 1 
90 percent confidence interval:
 31.93813 54.32395 
sample estimates:
event rate 

and similarly the other values by changing conf.level. If you do not want all the background information, try something like

> poisson.test(42, conf.level = 0.95 )$conf.int
[1] 30.26991 56.77180
[1] 0.95
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If the number of event is too small, it would be better to use the exact method.

exactPoiCI <- function (X, conf.level=0.95) {
  alpha = 1 - conf.level
  upper <- 0.5 * qchisq((1-(alpha/2)), (2*X))
  lower <- 0.5 * qchisq(alpha/2, (2*X +2))
  return(c(lower, upper))

Reference: Liddell FD. Simple exact analysis of the standardised mortality ratio. J Epidemiol Community Health. 1984;38:85-8.

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Welcome to the site. Do you mind expanding upon this. What exactly does "count data is too small" mean (sample size small or intensity of events is too small?) A reference would be appreciated as well. –  Andy W Dec 23 '11 at 13:09

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