calc_agreement: Coefficients comparing classification agreement
In m-clark/confusionMatrix: Confusion Matrix

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/calc_agreement.R

Computes several coefficients of agreement between the columns and rows of a 2-way contingency table. Most of the documentation for this function is copied directly from the matchClasses function documentation.

1	calc_agreement(tabble)

tabble

A 2-dimensional contingency table created with table.

Suppose we want to compare two classifications summarized by the contingency table T=[t_{ij}] where i,j=1,…,K and t_{ij} denotes the number of data points which are in class i in the first partition and in class j in the second partition. If both classifications use the same labels, then obviously the two classification agree completely if only elements in the main diagonal of the table are non-zero. On the other hand, large off-diagonal elements correspond to smaller agreement between the two classifications. If match.names is TRUE, the class labels as given by the row and column names are matched, i.e. only columns and rows with the same dimnames are used for the computation.

If the two classification do not use the same set of labels, or if identical labels can have different meaning (e.g., two outcomes of cluster analysis on the same data set), then the situation is a little bit more complicated. Let A denote the number of all pairs of data points which are either put into the same cluster by both partitions or put into different clusters by both partitions. Conversely, let D denote the number of all pairs of data points that are put into one cluster in one partition, but into different clusters by the other partition. Hence, the partitions disagree for all pairs D and agree for all pairs A. We can measure the agreement by the Rand index A/(A+D) which is invariant with respect to permutations of the columns or rows of T.

Both indices have to be corrected for agreement by chance if the sizes of the classes are not uniform.

In addition, the Phi coefficient, Yule coefficient, Peirce's science of the method (sometimes called the Youden's J index, though it predates Youden by 66 years), and Jaccard index are calculated. The first two are based on the approach in the psych package.

This function is called by confusion_matrix, but if this is all you want, you can simply supply the table to this function.

A tibble with:

`kappa`	`diag` corrected for agreement by chance.
`crand`	Rand index corrected for agreement by chance.

For Adjusted Rand code: Friedrich Leisch

Cohen. J. (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement.

Lawrence Hubert and Phipps Arabie (1985) Comparing partitions. Journal of Classification.

Stuart G Baker & Barnett S Kramer (2007) Peirce, Youden, and Receiver Operating Characteristic Curves, The American Statistician.

Peirce, C. S. (1884) "The Numerical Measure of the Success of Predictions", Science.

matchClasses, phi, Yule

 ## no class correlations: both kappa and crand almost zero

 g1 <- sample(1:5, size=1000, replace=TRUE)
 g2 <- sample(1:5, size=1000, replace=TRUE)

 tabble <- table(g1, g2)

 calc_agreement(tabble)

 ## let pairs (g1=1,g2=1) and (g1=3,g2=3) agree better

 k <- sample(1:1000, size=200)

 g1[k] <- 1
 g2[k] <- 1

 k <- sample(1:1000, size=200)

 g1[k] <- 3
 g2[k] <- 3

 tabble <- table(g1, g2)

 ## both kappa and crand should be significantly larger than before

 calc_agreement(tabble)