exactci (version 1.3-3)

exactci-package: Exact binomial and Poisson tests with Matching Confidence Intervals

Description

Calculates exact binomial and Poisson tests giving matching confidence intervals. There are 3 different methods for defining the two-sided p-values.

Details

Package:	exactci
Type:	Package
Version:	1.3-3
Date:	2017-09-28
License:	GPL2
LazyLoad:	yes

Although binom.test and poisson.test give exact tests and confidence intervals, for two-sided tests the confidence intervals (CI) are not formed by inverting the tests. Thus, there may be test-CI inconsistencies whereby the test rejects but the confidence interval contains the null parameter. The exactci package eliminates many of these test-CI inconsistencies for two-sided tests, by outputing the matching confidence interval with each test. The package uses one of three different methods for defining the two-sided p-value. The main functions of the package are binom.exact and poisson.exact which follow the same format as binom.test and poisson.test except have the option 'tsmethod' to define the two-sided method for calculating the p-values, and give matching confidence intervals (i.e., ones that come from the inversion of the p-values).

The package also has options for mid-p values.

References

Blaker, H. (2000) Confidence curves and improved exact confidence intervals for discrete distributions. Canadian Journal of Statistics 28: 783-798.

Fay, M. P. (2010). Confidence intervals that Match Fisher's exact and Blaker's exact tests. Biostatistics. 11:373-374.

Fay, M.P. (2010). Two-sided Exact Tests and Matching Confidence Intervals for Discrete Data. R Journal 2(1): 53-58.

Hirjim K. F. (2006). Exact analysis of discrete data. Chapman and Hall/CRC. New York.

See Also

For comparisons of two binomial groups see exact2x2

Examples

# NOT RUN {
## Note binom.test calculates p-values using priciple of minimum likelihood
## while it calculates the central confidence intervals. That is why the 
## inferences do not match in this example.
binom.test(10,12,p=20000/37877)
binom.exact(10,12,p=20000/37877,tsmethod="minlike")
binom.exact(10,12,p=20000/37877,tsmethod="central")
## We also allow the method studied in Blaker (2000)
binom.exact(10,12,p=20000/37877,tsmethod="blaker")
# }