Computation of confidence intervals for binomial proportions and for difference of binomial proportions.
Reason this release was yanked:
computation of 1-sided confidence intervals has bugs
Project description
Confidence Intervals for Difference of Binomial Proportions
Computation of confidence intervals for binomial proportions and for difference of binomial proportions.
Installation
Run
python -m pip install diff-binom-confint
or install the latest version in GitHub using
python -m pip install git+https://github.com/DeepPSP/DBCI.git
or git clone this repository and install locally via
cd DBCI
python -m pip install .
Usage examples
from diff_binom_confint import compute_difference_confidence_interval
n_positive, n_total = 84, 101
ref_positive, ref_total = 89, 105
confint = compute_difference_confidence_interval(
n_positive,
n_total,
ref_positive,
ref_total,
conf_level=0.95,
method="wilson",
)
Implemented methods
Confidence intervals for binomial proportions
Click to view!
Method (type) | Implemented |
---|---|
wilson | :heavy_check_mark: |
wilson-cc | :heavy_check_mark: |
wald | :heavy_check_mark: |
wald-cc | :heavy_check_mark: |
agresti-coull | :heavy_check_mark: |
jeffreys | :heavy_check_mark: |
clopper-pearson | :heavy_check_mark: |
arcsine | :heavy_check_mark: |
logit | :heavy_check_mark: |
pratt | :heavy_check_mark: |
witting | :heavy_check_mark: |
mid-p | :heavy_check_mark: |
lik | :heavy_check_mark: |
blaker | :heavy_check_mark: |
modified-wilson | :heavy_check_mark: |
modified-jeffreys | :heavy_check_mark: |
Confidence intervals for difference of binomial proportions
Click to view!
Method (type) | Implemented |
---|---|
wilson | :heavy_check_mark: |
wilson-cc | :heavy_check_mark: |
wald | :heavy_check_mark: |
wald-cc | :heavy_check_mark: |
haldane | :heavy_check_mark: |
jeffreys-perks | :heavy_check_mark: |
mee | :heavy_check_mark: |
miettinen-nurminen | :heavy_check_mark: |
true-profile | :heavy_check_mark: |
hauck-anderson | :heavy_check_mark: |
agresti-caffo | :heavy_check_mark: |
brown-li | :heavy_check_mark: |
brown-li-jeffrey | :heavy_check_mark: |
miettinen-nurminen-brown-li | :heavy_check_mark: |
exact | :x: |
mid-p | :x: |
santner-snell | :x: |
chan-zhang | :x: |
agresti-min | :x: |
wang | :x: |
pradhan-banerjee | :x: |
References
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
-
taken from Reference 1 for automatic test of the correctness of the implementation of the algorithms.
-
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"
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