cipoisson {survival} R Documentation

## Confidence limits for the Poisson

### Description

Confidence interval calculation for Poisson rates.

### Usage

cipoisson(k, time = 1, p = 0.95, method = c("exact", "anscombe"))


### Arguments

 k Number of successes time Total time on trial p Probability level for the (two-sided) interval method The method for computing the interval.

### Details

The likelihood method is based on equation 10.10 of Feller, which relates poisson probabilities to tail area of the gamma distribution. The Anscombe approximation is based on the fact that sqrt(k + 3/8) has a nearly constant variance of 1/4, along with a continuity correction.

There are many other proposed intervals: Patil and Kulkarni list and evaluate 19 different suggestions from the literature!. The exact intervals can be overly broad for very small values of k, many of the other approaches try to shrink the lengths, with varying success.

### Value

a vector, matrix, or array. If both k and time are single values the result is a vector of length 2 containing the lower an upper limits. If either or both are vectors the result is a matrix with two columns. If k is a matrix or array, the result will be an array with one more dimension; in this case the dimensions and dimnames (if any) of k are preserved.

### References

F.J. Anscombe (1949). Transformations of Poisson, binomial and negative-binomial data. Biometrika, 35:246-254.

W.F. Feller (1950). An Introduction to Probability Theory and its Applications, Volume 1, Chapter 6, Wiley.

V. V. Patil and H.F. Kulkarni (2012). Comparison of confidence intervals for the poisson mean: some new aspects. Revstat 10:211-227.

ppois, qpois

### Examples

cipoisson(4) # 95\% confidence limit
# lower    upper
# 1.089865 10.24153
ppois(4, 10.24153)     #chance of seeing 4 or fewer events with large rate
#  0.02500096
1-ppois(3, 1.08986)    #chance of seeing 4 or more, with a small rate
#  0.02499961



[Package survival version 3.5-5 Index]