| Type: | Package |
| Title: | Exact Rate Ratio Test |
| Version: | 1.1 |
| Date: | 2022-05-09 |
| Author: | Michael Fay <mfay@niaid.nih.gov> |
| Maintainer: | Michael Fay <mfay@niaid.nih.gov> |
| Depends: | R (≥ 2.4.1), stats |
| Description: | Performs exact rate ratio tests. |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Packaged: | 2022-05-09 14:03:26 UTC; faym |
| Repository: | CRAN |
| Date/Publication: | 2022-05-09 14:50:02 UTC |
An Exact Rate Ratio Test Assuming Poisson Counts
Description
Performs the uniformy most powerful unbiased test on the ratio of rates of two Poisson counts with given time (e.g., perons-years) at risk for each count.
Usage
rateratio.test(x, n, RR = 1,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95)
Arguments
x |
a vector of length 2 with counts for the two rates |
n |
a vector of length 2 with time at risk in each rate |
RR |
the null rate ratio (two.sided) or the rate ratio on boundary between null and alternative |
alternative |
a character string specifying the alternative hypothesis, must be one of '"two.sided"' (default), '"greater"' or '"less"'. You can specify just the initial letter. |
conf.level |
confidence level of the returned confidence interval. Must be a single number between 0 and 1. |
Details
The rateratio.test tests whether the ratio of the first rate (estimated by x[1]/n[1])
over the second rate (estimated by x[2]/n[2]) is either equal to, less, or greater than
RR. Exact confidence intervals
come directly from binom.test. The two-sided p-value is defined as either 1 or twice the minimum of
the one-sided p-values. See Lehmann (1986, p. 152) or vignette("rateratio.test").
For full discussion of the p-value and confidence interval consistency of inferences, see Fay (2010) and exactci package.
Value
An object of class ‘htest’ containing the following components:
p.value |
the p-value of the test |
estimate |
a vector with the rate ratio and the two individual rates |
null.value |
the null rate ratio (two.sided) or the rate ratio on boundary between null and alternative |
conf.int |
confidence interval |
alternative |
type of alternative hypothesis |
method |
description of method |
data.name |
description of data |
Note
Much of the error checking code was taken from prop.test.
Author(s)
Michael Fay
References
Fay, M. P. (2010). Two-sided exact tests and matching confidence intervals for discrete data. R Journal, 2(1), 53-58.
Lehmann, E.L. (1986). Testing Statistical Hypotheses (second edition). Wadsworth and Brooks/Cole, Pacific Grove, California.
See Also
See poisson.exact in the exactci package, which gives the same test.
Examples
### p values and confidence intervals are defined the same way
### so there is consistency in inferences
rateratio.test(c(2,9),c(17877,16660))
### Small counts and large time values will give results similar to Fisher's exact test
### since in that case the rate ratio is approximately equal to the odds ratio
### However, for the Fisher's exact test, the two-sided p-value is defined differently from
### the way the confidence intervals are defined and may imply different inferences
### i.e., p-value may say reject OR=1, but confidence interval says not to reject OR=1
fisher.test(matrix(c(2,9,17877-2,16660-9),2,2))