> 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/pinball-loss/pinball.numeric.md).

# pinball.numeric.md

|                 |                 |
| --------------- | --------------: |
| pinball.numeric | R Documentation |

### Pinball Loss

#### Description

A generic S3 function to compute the *pinball loss* score for a\
regression model. This function dispatches to S3 methods in `pinball()`\
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 `pinball()` 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 `pinball()` in a "safe" validator that\
checks for NA values and matching length, for example:

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

```r
safe_pinball <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  pinball(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'
pinball(actual, predicted, alpha = 0.5, deviance = FALSE, ...)
```

#### Arguments

|                       |                                                                                               |
| --------------------- | --------------------------------------------------------------------------------------------- |
| `actual`, `predicted` | A pair of \<double> vectors of length `n`.                                                    |
| `alpha`               | A \<double>-value of length `1` (default: `0.5`). The slope of the pinball loss function.     |
| `deviance`            | A \<logical>-value of length 1 (default: FALSE). If TRUE the function returns the `D^2` loss. |
| `...`                 | 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)

## Evaluate performance
SLmetrics::pinball(
   actual    = actual_values, 
   predicted = predicted_values
)
```

```

</div>

```


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