Calculate the root mean squared error. `rmse()`

is a metric that is in
the same units as the original data.

rmse(data, ...)
# S3 method for data.frame
rmse(data, truth, estimate, na_rm = TRUE, ...)
rmse_vec(truth, estimate, na_rm = TRUE, ...)

## Arguments

data |
A `data.frame` containing the `truth` and `estimate`
columns. |

... |
Not currently used. |

truth |
The column identifier for the true results
(that is `numeric` ). This should be an unquoted column name although
this argument is passed by expression and supports
quasiquotation (you can unquote column
names). For `_vec()` functions, a `numeric` vector. |

estimate |
The column identifier for the predicted
results (that is also `numeric` ). As with `truth` this can be
specified different ways but the primary method is to use an
unquoted variable name. For `_vec()` functions, a `numeric` vector. |

na_rm |
A `logical` value indicating whether `NA`
values should be stripped before the computation proceeds. |

## Value

A `tibble`

with columns `.metric`

, `.estimator`

,
and `.estimate`

and 1 row of values.

For grouped data frames, the number of rows returned will be the same as
the number of groups.

For `rmse_vec()`

, a single `numeric`

value (or `NA`

).

## See also

Other numeric metrics: `ccc`

,
`huber_loss_pseudo`

,
`huber_loss`

, `iic`

,
`mae`

, `mape`

,
`mase`

, `rpd`

,
`rpiq`

, `rsq_trad`

,
`rsq`

, `smape`

Other accuracy metrics: `ccc`

,
`huber_loss_pseudo`

,
`huber_loss`

, `iic`

,
`mae`

, `mape`

,
`mase`

, `smape`

## Examples

# Supply truth and predictions as bare column names
rmse(solubility_test, solubility, prediction)

#> # A tibble: 1 x 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 rmse standard 0.722

#> # A tibble: 10 x 4
#> resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 1 rmse standard 0.715
#> 2 10 rmse standard 0.673
#> 3 2 rmse standard 0.716
#> 4 3 rmse standard 0.644
#> 5 4 rmse standard 0.737
#> 6 5 rmse standard 0.675
#> 7 6 rmse standard 0.807
#> 8 7 rmse standard 0.801
#> 9 8 rmse standard 0.635
#> 10 9 rmse standard 0.692

#> # A tibble: 1 x 1
#> avg_estimate
#> <dbl>
#> 1 0.710