> For the complete documentation index, see [llms.txt](https://slmetrics-docs.gitbook.io/v1/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://slmetrics-docs.gitbook.io/v1/reference/regression-metrics/concordance-correlation-coefficient/weighted.ccc.numeric.md).

# weighted.ccc.numeric.md

|                      |                 |
| -------------------- | --------------: |
| weighted.ccc.numeric | R Documentation |

### 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:

{% code overflow="wrap" lineNumbers="true" %}

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

{% endcode %}

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

#### Usage

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

#### 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                                                                                           |

#### Value

A \<double> value

#### 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.

#### Examples

```r
## 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
)
```

```

</div>

```


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