Proportion overall accuracy of a confusion matrix

Calculates the overall accuracy and Cohen's kappa statistics of a confusion matrix.

Usage

proportion_accurate(observed, predicted)

Arguments

observed: Vector of observed variables. These are the true class labels.
predicted: Vector of predicted variables. These are the predicted class labels.

Details

This function evaluates the performance of a confusion matrix by calculating the overall accuracy and Cohen's kappa statistics.

The function performs the following steps: 1. Computes the confusion matrix from the observed and predicted values. 2. Calculates the diagonal proportion (overall accuracy) and the off-diagonal proportion. 3. Computes Cohen's kappa statistics (unweighted,linear,and squared weights).

The output is a data.frame containing the following metrics: -`cm_diagonal`: Proportion of correct classifications (diagonal elements). -`cm_off_diagonal`: Proportion of misclassified observations (off-diagonal elements). -`kappa_unweighted`: Cohen's kappa statistic with no weights. -`kappa_linear`: Cohen's kappa statistic with linear weights. -`kappa_squared`: Cohen's kappa statistic with squared weights.

Examples

# Example with numeric observed and predicted values
proportion_accurate(observed=c(1,2,3,4,5,10),predicted=c(1,2,3,4,5,11))
#>   cm_diagonal cm_off_diagonal kappa_unweighted kappa_linear kappa_squared
#> 1      0.8333               1           0.8065       0.9286        0.9801