Calculate the mean absolute scaled error. This metric is *scale independent*
and *symmetric*. It is generally used for comparing forecast error in
time series settings. Due to the time series nature of this metric, it
is neccesary to order observations in ascending order by time.

mase(data, ...) # S3 method for data.frame mase(data, truth, estimate, m = 1L, mae_train = NULL, na_rm = TRUE, ...) mase_vec(truth, estimate, m = 1L, mae_train = NULL, na_rm = TRUE, ...)

data | A |
---|---|

... | Not currently used. |

truth | The column identifier for the true results
(that is |

estimate | The column identifier for the predicted
results (that is also |

m | An integer value of the number of lags used to calculate the
in-sample seasonal naive error. The default is used for non-seasonal time
series. If each observation was at the daily level and the data showed weekly
seasonality, then |

mae_train | A numeric value which allows the user to provide the
in-sample seasonal naive mean absolute error. If this value is not provided,
then the out-of-sample seasonal naive mean absolute error will be calculated
from |

na_rm | A |

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 `mase_vec()`

, a single `numeric`

value (or `NA`

).

`mase()`

is different from most numeric metrics. The original implementation
of `mase()`

calls for using the *in-sample* naive mean absolute error to
compute scaled errors with. It uses this instead of the out-of-sample error
because there is a chance that the out-of-sample error cannot be computed
when forecasting a very short horizon (i.e. the out of sample size is only
1 or 2). However, `yardstick`

only knows about the out-of-sample `truth`

and
`estimate`

values. Because of this, the out-of-sample error is used in the
computation by default. If the in-sample naive mean absolute error is
required and known, it can be passed through in the `mae_train`

argument
and it will be used instead. If the in-sample data is available, the
naive mean absolute error can easily be computed with
`mae(data, truth, lagged_truth)`

.

Rob J. Hyndman (2006). ANOTHER LOOK AT FORECAST-ACCURACY METRICS FOR
INTERMITTENT DEMAND. *Foresight*, 4, 46.

Other numeric metrics:
`ccc()`

,
`huber_loss_pseudo()`

,
`huber_loss()`

,
`iic()`

,
`mae()`

,
`mape()`

,
`rmse()`

,
`rpd()`

,
`rpiq()`

,
`rsq_trad()`

,
`rsq()`

,
`smape()`

Other accuracy metrics:
`ccc()`

,
`huber_loss_pseudo()`

,
`huber_loss()`

,
`iic()`

,
`mae()`

,
`mape()`

,
`rmse()`

,
`smape()`

# Supply truth and predictions as bare column names mase(solubility_test, solubility, prediction)#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 mase standard 3.56library(dplyr) set.seed(1234) size <- 100 times <- 10 # create 10 resamples solubility_resampled <- bind_rows( replicate( n = times, expr = sample_n(solubility_test, size, replace = TRUE), simplify = FALSE ), .id = "resample" ) # Compute the metric by group metric_results <- solubility_resampled %>% group_by(resample) %>% mase(solubility, prediction) metric_results#> # A tibble: 10 x 4 #> resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 1 mase standard 0.256 #> 2 10 mase standard 0.240 #> 3 2 mase standard 0.238 #> 4 3 mase standard 0.219 #> 5 4 mase standard 0.229 #> 6 5 mase standard 0.261 #> 7 6 mase standard 0.217 #> 8 7 mase standard 0.267 #> 9 8 mase standard 0.216 #> 10 9 mase standard 0.251#> # A tibble: 1 x 1 #> avg_estimate #> <dbl> #> 1 0.240