Statistical Calculators

Statistics tools, including a kappa calculator and a Lin's concordance calculator.

Requirements

• Python 3.x
• Python dependencies (see requirements.txt for a complete listing)
• Windows, MacOS, Linux or any other *nix OS

This project uses Python 3.x unittest module for tests. You may need to install the requirements first. Also, if you would like to view the test coverage, please install the coverage module using pip install coverage. For more information check the pip manual (pip -h).

To fetch the libraries that this code depends on, run this command within the statcalc folder

pip install -r requirements.txt

For running the tests.

python3 test.py

For measuring the coverage and producing an HTML report.

coverage run tests.py
coverage html

For updating the coverage badge, run the commands above, and then (after installing the coverage-badge module) run the coverage-badge command.

coverage-badge -o coverage.svg

Kappa calculator

Sample usage for kappa.py:

$ python kappa.py --npp 1.0 --npa 1.0 --nap 2.0 --naa 2.0 --kappatest 0.5
Results for 2x2 Interrater table
+-----------+-----------+--------+
| Rater A   | Rater B   | None   |
+===========+===========+========+
|           | present   | absent |
+-----------+-----------+--------+
| present   | 1         | 1      |
+-----------+-----------+--------+
| absent    | 2         | 2      |
+-----------+-----------+--------+
+-------------------------------------------------------------+
| kappahat = 0.0, (kappa+ = 0.0. kappa- = 0.0)                |
+-------------------------------------------------------------+
| s.e.(0) = 0.3849,  s.e.(kappahat) = 0.3849                  |
+-------------------------------------------------------------+
|                                                             |
+-------------------------------------------------------------+
| Hypothesis test p-values                                    |
+-------------------------------------------------------------+
| One-sided test, H0 is kappa =<0                             |
+-------------------------------------------------------------+
| p = Prob[>kappahat, given that kappa=0] = 0.5000            |
+-------------------------------------------------------------+
|                                                             |
+-------------------------------------------------------------+
| One-sided test, H0 is kappa =< 0.5                          |
+-------------------------------------------------------------+
| p = Prob[>kappahat, given that kappa= 0.5] = 0.9030         |
+-------------------------------------------------------------+
|                                                             |
+-------------------------------------------------------------+
| Two-sided test, H0 is kappa= 0.5                            |
+-------------------------------------------------------------+
| p = Prob[>|kappahat-kappa|, given that kappa= 0.5] = 0.1939 |
+-------------------------------------------------------------+

Sample usage for kappa_simple.py:

$ python kappa_simple.py --npp 1.0 --npa 1.0 --nap 2.0 --naa 2.0 --kappatest 0.5
Results for 2x2 Interrater table
+-----------+-----------+--------+
| Rater A   | Rater B   | None   |
+===========+===========+========+
|           | present   | absent |
+-----------+-----------+--------+
| present   | 1         | 1      |
+-----------+-----------+--------+
| absent    | 2         | 2      |
+-----------+-----------+--------+
+------------------------------------------------------------------+
| estimated kappa = 0.0                                            |
+------------------------------------------------------------------+
| s.e.(0) = 0.3849,  s.e.(estimated kappa) = 0.3849                |
+------------------------------------------------------------------+
| Hypothesis test p-values                                         |
+------------------------------------------------------------------+
| One-sided test, H0 is kappa <= 0.5                               |
+------------------------------------------------------------------+
| p = Prob[>estimated kappa, given that kappa= 0.90300.5] = 0.9030 |
+------------------------------------------------------------------+

Try python kappa.py -h for more or python kappa_simple.py -h.

Lin's Concordance calculator

Sample usage for links_concordance.py:

$ python lins_concordance.py "1~2~2~3~3~4"
+----------------------------------------------------------+
| Concordance Results                                      |
+==========================================================+
| Sample concordance correlation coefficient (pc) = 0.5714 |
+----------------------------------------------------------+
| Lower one-sided 95% CL for pc = -0.193                   |
+----------------------------------------------------------+
| Lower two-sided 95% CL for pc = -0.343                   |
+----------------------------------------------------------+
| Upper one-sided 95% CL for pc = 0.9043                   |
+----------------------------------------------------------+
| Upper two-sided 95% CL for pc = 0.9298                   |
+----------------------------------------------------------+

Try python lins_concordance.py -h for more.