# Root Mean Squared Error (RMSE)

### Description

Compute root mean squared error of fitted linear (mixed effects) models.

### Usage

1 | ```
rmse(fit, normalized = FALSE)
``` |

### Arguments

`fit` |
Fitted linear model of class |

`normalized` |
Logical, use |

### Value

The root mean squared error of `fit`

; or the normalized
root mean squared error of `fit`

if `normalized = TRUE`

.

### Note

The RMSE is the square root of the variance of the residuals and indicates
the absolute fit of the model to the data (difference between observed data
to model's predicted values). “RMSE can be interpreted as the standard
deviation of the unexplained variance, and has the useful property
of being in the same units as the response variable. Lower values
of RMSE indicate better fit. RMSE is a good measure of how accurately
the model predicts the response, and is the most important criterion
for fit if the main purpose of the model is prediction.”
(Grace-Martin K: Assessing the Fit of Regression Models)

The normalized RMSE is the proportion of the RMSE related to the
range of the response variable. Hence, lower values indicate
less residual variance.

### References

### See Also

`cv`

for the coefficient of variation, and
`rse`

for the residual standard error.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 |