SLmetrics (version 0.3-4)

weighted.ccc.numeric: Concordance Correlation Coefficient

Description

A generic S3 function to compute the concordance correlation coefficient score for a regression model. This function dispatches to S3 methods in ccc() and performs no input validation. If you supply NA values or vectors of unequal length (e.g. length(x) != length(y)), the underlying C++ code may trigger undefined behavior and crash your R session.

Defensive measures

Because ccc() operates on raw pointers, pointer-level faults (e.g. from NA or mismatched length) occur before any R-level error handling. Wrapping calls in try() or tryCatch() will not prevent R-session crashes.

To guard against this, wrap ccc() in a "safe" validator that checks for NA values and matching length, for example:

safe_ccc <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  ccc(x, y, ...)
}

Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++ code.

Usage

# S3 method for numeric
weighted.ccc(actual, predicted, w, correction = FALSE, ...)

Value

A <double> value

Arguments

actual, predicted

A pair of <double> vectors of length \(n\).

w

A <double> vector of sample weights.

correction

A <logical> vector of length \(1\) (default: FALSE). If TRUE the variance and covariance will be adjusted with \(\frac{1-n}{n}\)

...

Arguments passed into other methods

References

James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.

See Also

Other Regression: deviance.gamma(), deviance.poisson(), deviance.tweedie(), gmse(), huberloss(), maape(), mae(), mape(), mpe(), mse(), pinball(), rae(), rmse(), rmsle(), rrmse(), rrse(), rsq(), smape()

Other Supervised Learning: accuracy(), auc.pr.curve(), auc.roc.curve(), baccuracy(), brier.score(), ckappa(), cmatrix(), cross.entropy(), deviance.gamma(), deviance.poisson(), deviance.tweedie(), dor(), fbeta(), fdr(), fer(), fmi(), fpr(), gmse(), hammingloss(), huberloss(), jaccard(), logloss(), maape(), mae(), mape(), mcc(), mpe(), mse(), nlr(), npv(), pinball(), plr(), pr.curve(), precision(), rae(), recall(), relative.entropy(), rmse(), rmsle(), roc.curve(), rrmse(), rrse(), rsq(), shannon.entropy(), smape(), specificity(), zerooneloss()

Examples

## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Generate sample
## weights
sample_weights <- c(0.3, 0.5, 0.3, 0, 0.8, 0.8, 1)

## Evaluate performance
SLmetrics::weighted.ccc(
   actual    = actual_values, 
   predicted = predicted_values,
   w         = sample_weights
)