Performs the uniformly most powerful unbiased test on the ratio of rates of two Poisson counts with given time (e.g., persons-years) at risk for each count. This module is a python re-implement of the rateratio.test R package.
from rateratio import test as rateratio_test
rateratio_test(x, n, RR = 1,
alternative = "two.sided",
conf_level = 0.95)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.
The rateratio.test tests whether the ratio of the first rate (estimated by x[0]/n[0] over the second rate (estimated by x[1]/n[1]) is either equal to, less, or greater than RR. 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). For full discussion of the p-value and confidence interval consistency of inferences, see Fay (2010).
An object of class "htest" containing the following components:
p_value The p-value of the test. Due to numerical restriction,
the smallest p-value that can be returned is 2.22E-16.
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.
methods Description of method.
data_name Description of data.
- Wei, X., Zhang, C., Freddolino, P. L., & Zhang, Y. (2020). Detecting Gene Ontology misannotations using taxon-specific rate ratio comparisons. Bioinformatics, 36(16), 4383-4388.
- Fay, M. P. (2010). Two-sided exact tests and matching confidence intervals for discrete data. R Journal, 2(1), 53-58.
The following python code
from rateratio import test as rateratio_test
print(rateratio_test((2,9), (17877,16660)))will output
Exact Rate Ratio Test, assuming Poisson counts
data: c(2, 9) with time of c(17877, 16660), null rate ratio 1
p-value = 0.0501064956141
alternative hypothesis: true rate ratio is not equal to 1
95.0 percent confidence interval:
0.0217740627591 1.00054910449
sample estimates:
Rate Ratio Rate 1 Rate 2
0.207094155743 0.000111875594339 0.000540216086435