# mse.md

|     |                 |
| --- | --------------: |
| mse | R Documentation |

### Mean Squared Error

#### Description

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

{% code overflow="wrap" lineNumbers="true" %}
```r
safe_mse <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  mse(x, y, ...)
}
```
{% endcode %}

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

#### Usage

```r
## Generic S3 method
## for Mean Squared Error
mse(...)

## Generic S3 method
## for weighted Mean Squared Error
weighted.mse(...)
```

#### Arguments

| `...` | <p>Arguments passed on to <code>mse.numeric</code>,<code>weighted.mse.numeric</code></p><p><code>actual,predicted</code></p><p>A pair of &#x3C;double> vectors of length <code>n</code>.</p><p><code>w</code></p><p>A &#x3C;double> vector of sample weights.</p> |
| ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

#### 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.

#### 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::mse(
   actual    = actual_values, 
   predicted = predicted_values
)
```

```

</div>

```
