Trimmed root mean squared error

The root mean squared error of the 1 - trim fraction of the observations with the smallest absolute errors. When the reference values contain outliers, they also appear in the assessment sets of a resampling scheme, where their errors would dominate the usual RMSE and favor non-robust models. The trimmed RMSE leaves them out, like the trimmed standard error of prediction used to tune partial robust M-regression (Serneels et al., 2005), so it is the metric to use when tuning robust LW-PLS models (lwpls() with robust = TRUE) on contaminated data.

Usage

trimmed_rmse(data, ...)

# S3 method for class 'data.frame'
trimmed_rmse(
  data,
  truth,
  estimate,
  trim = 0.2,
  na_rm = TRUE,
  case_weights = NULL,
  ...
)

trimmed_rmse_vec(
  truth,
  estimate,
  trim = 0.2,
  na_rm = TRUE,
  case_weights = NULL,
  ...
)

Arguments

data

A data frame containing the columns given by truth and estimate.

...

Not currently used.

truth

The column identifier for the true results (numeric).

estimate

The column identifier for the predicted results (numeric).

trim

The fraction of observations with the largest absolute errors to leave out, in [0, 1). The default, 0.2, leaves out 20%.

na_rm

A logical: should missing values be removed?

case_weights

The optional column identifier for case weights.

Value

A tibble with columns .metric, .estimator and .estimate and one row (or one row per group), or, for trimmed_rmse_vec(), a single number.

References

Serneels, S., Croux, C., Filzmoser, P. and Van Espen, P. J. (2005). Partial robust M-regression. Chemometrics and Intelligent Laboratory Systems, 79(1–2), 55–64. doi:10.1016/j.chemolab.2005.04.007

Examples

df <- data.frame(truth = c(1, 2, 3, 4, 5), estimate = c(1.1, 2.1, 2.9, 4.2, 15))
trimmed_rmse(df, truth, estimate)
#> # A tibble: 1 × 3
#>   .metric      .estimator .estimate
#>   <chr>        <chr>          <dbl>
#> 1 trimmed_rmse standard       0.132
yardstick::rmse(df, truth, estimate)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 rmse    standard        4.47

# In a metric set, for example with tune::tune_grid()
metrics <- yardstick::metric_set(trimmed_rmse, yardstick::rmse)
metrics(df, truth, estimate)
#> # A tibble: 2 × 3
#>   .metric      .estimator .estimate
#>   <chr>        <chr>          <dbl>
#> 1 trimmed_rmse standard       0.132
#> 2 rmse         standard       4.47