diff-binom-confint 0.0.1

Computation of confidence intervals for binomial proportions and for difference of binomial proportions.

Confidence Intervals for Difference of Binomial Proportions

Computation of confidence intervals for binomial proportions and for difference of binomial proportions.

References

1. SAS
2. PASS
3. statsmodels.stats.proportion
4. scipy.stats._binomtest
5. corplingstats
6. DescTools.StatsAndCIs

NOTE

Reference 1 has errors in the description of the methods Wilson CC, Mee, Miettinen-Nurminen. The correct computation of Wilson CC is given in Reference 5. The correct computation of Mee, Miettinen-Nurminen are given in the code blocks in Reference 1

Test data

Test data are

1. taken from Reference 1 for automatic test of the correctness of the implementation of the algorithms.

2. generated using DescTools.StatsAndCIs via

   library("DescTools")
   library("data.table")
   
   results = data.table()
   for (m in c("wilson", "wald", "waldcc", "agresti-coull", "jeffreys",
                   "modified wilson", "wilsoncc","modified jeffreys",
                   "clopper-pearson", "arcsine", "logit", "witting", "pratt", 
                   "midp", "lik", "blaker")){
       ci = BinomCI(84,101,method = m)
       new_row = data.table("method" = m, "ratio"=ci[1], "lower_bound" = ci[2], "upper_bound" = ci[3])
       results = rbindlist(list(results, new_row))
   }
   fwrite(results, "./test/test-data/example-84-101.csv")  # with manual slight adjustment of method names
   

The filenames has the following pattern:

# for computing confidence interval for difference of binomial proportions
"example-(?P<n_positive>[\\d]+)-(?P<n_total>[\\d]+)-vs-(?P<ref_positive>[\\d]+)-(?P<ref_total>[\\d]+)\\.csv"

# for computing confidence interval for binomial proportions
"example-(?P<n_positive>[\\d]+)-(?P<n_total>[\\d]+)\\.csv"