mcc: Matthews correlation coefficient
In nestedcv: Nested Cross-Validation with 'glmnet' and 'caret'
mcc R Documentation
Matthews correlation coefficient
Description
Calculates Matthews correlation coefficient (MCC) which is in essence a
correlation coefficient between the observed and predicted binary
classifications. It has also been generalised to multi-class classification.
Usage
mcc(cm)
mcc_multi(cm)
Arguments
cm
A contingency table or matrix of predicted vs observed classes with
reference classes in columns and predicted classes in rows.
Details
Use mcc() for 2x2 tables (binary classification). mcc_multi() is
for multi-class classification with k x k tables and is calculated using
Gorodkin's method.
Value
Returns a value between -1 and +1. A coefficient of +1
represents a perfect prediction, 0 no better than random prediction and -1
indicates total disagreement between prediction and observation.
References
Gorodkin, J. (2004). Comparing two K-category assignments by a
K-category correlation coefficient. Computational Biology and Chemistry. 28
(5): 367–374.
nestedcv documentation built on Sept. 11, 2024, 5:22 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| mcc | R Documentation |
Matthews correlation coefficient
Description
Calculates Matthews correlation coefficient (MCC) which is in essence a correlation coefficient between the observed and predicted binary classifications. It has also been generalised to multi-class classification.
Usage
mcc(cm)
mcc_multi(cm)
Arguments
cm |
A contingency table or matrix of predicted vs observed classes with reference classes in columns and predicted classes in rows. |
Details
Use mcc() for 2x2 tables (binary classification). mcc_multi() is
for multi-class classification with k x k tables and is calculated using
Gorodkin's method.
Value
Returns a value between -1 and +1. A coefficient of +1 represents a perfect prediction, 0 no better than random prediction and -1 indicates total disagreement between prediction and observation.
References
Gorodkin, J. (2004). Comparing two K-category assignments by a K-category correlation coefficient. Computational Biology and Chemistry. 28 (5): 367–374.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.