https://github.com/adamrossnelson/conmtrx
Produces confusion matrix and related statistics.
https://github.com/adamrossnelson/conmtrx
bayes bayesian-inference classification-algorithm stata
Last synced: 3 months ago
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Produces confusion matrix and related statistics.
- Host: GitHub
- URL: https://github.com/adamrossnelson/conmtrx
- Owner: adamrossnelson
- License: mit
- Created: 2017-10-02T20:50:03.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-05-09T16:48:59.000Z (about 8 years ago)
- Last Synced: 2025-11-21T15:04:24.084Z (7 months ago)
- Topics: bayes, bayesian-inference, classification-algorithm, stata
- Language: Stata
- Homepage:
- Size: 42 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# conmtrx.ado
Produces confusion matrix and related statistics.
This code is an extension of: https://ideas.repec.org/c/boc/bocode/s458127.html
Also inspired by https://artax.karlin.mff.cuni.cz/r-help/library/caret/html/confusionMatrix.html
Another related resource https://www.statalist.org/forums/forum/general-stata-discussion/general/1371221-n-by-k-contingency-table-for-two-categorical-variable-sensitivity-specificity-ppv-npv-accuracy-calculations
## See Also
The `conmtrx` package is a wrapper for `classtabi`. Another package, `conrpt` provides more flexibility and additional options.
* [Classtabi Information](https://ideas.repec.org/c/boc/bocode/s458127.html)
* [Conrpt Information](https://github.com/adamrossnelson/conrpt)
## Installation
Use the following to install from command line:
```Stata
net install conmtrx, from(https://raw.githubusercontent.com/adamrossnelson/conmtrx/master)
```
Use the following to check for installation (`which` command works to check for any package). Use `ado` command to list all installed packages.
```Stata
which classtabi3
which conmtrx
```
## Usage
```
conmtrx rowvar(binary) colvar(binary)[, ROWlabel(string) COLlabel(string) VARlab(yes)]
```
Where rowvar is generally the predicted outcome while colvar is generally the actual outcome. Optionally `conmtrx` accepts a `ROWlabel(string)` and `COLlabel(string)`.
Alternatively, `conmtrx` also accepts a `VARlab(yes)` option which will instruct conmtrx to use `rowvar(binary)` and `colvar(binary)` value lables.
If row or column title specified, and `VARlab(yes)` not specified, the defaults are `Classification Test Result` and `Reference Classification`.
Previously with `classtabi' the syntax was:
```Stata
classtabi #a #b #c #d [, rowlabel(string) collabel(string)]
```
Where each of the four figures (`#a #b #c #d`) represente one of the four probabilities.
| Negatives | Positives |
|-----------------------------------------|--------------------------------------------|
| #a -- disease=0, test=0 (true negative) | #b -- disease=0, test=1 (false positive) |
| #c -- disease=1, test=0 (false negative)| #d -- disease=1, test=1 (true positive) |
## Also requires
Erlier versions required `distinct` which can be installed with `ssc install distinct` and `classtabi` also available using `ssc install classtabi`.
Revised versions check for binary status using `capture confirm capture confirm numeric variable varname` and `capture assert varname == 1 | varname == 0`.
## Demonstration output
```
. conmtrx prUnd isUnd
Specified variables binary. Producing confusion matrix.
+----------------+
| Key |
|----------------|
| frequency |
| row percentage |
+----------------+
Predicted |
Under | Actual Under
| 0 1 | Total
-----------+----------------------+----------
0 | 36 60 | 96
| 37.50 62.50 | 100.00
-----------+----------------------+----------
1 | 2 2 | 4
| 50.00 50.00 | 100.00
-----------+----------------------+----------
Total | 38 62 | 100
| 38.00 62.00 | 100.00
| Actual
Predicted | 0 1 | Total
-----------+----------------------+----------
0 | 36 60 | 96
1 | 2 2 | 4
-----------+----------------------+----------
Total | 38 62 | 100
-------------------------------------------------
Sensitivity D/(C+D) 50.00%
Specificity A/(A+B) 37.50%
Positive predictive value D/(B+D) 3.23%
Negative predictive value A/(A+C) 94.74%
-------------------------------------------------
False positive rate B/(A+B) 62.50%
False negative rate C/(C+D) 50.00%
-------------------------------------------------
Correctly classified A+C/(A+B+C+D) 38.00%
-------------------------------------------------
Effect strength for sensitivity -12.50%
-------------------------------------------------
ROC area 0.4375
-------------------------------------------------
- conmtrx - Command was a succss.
```
## Known limitation
Does not support `if` and `in` options/statements.