mpe function

Mean percentage error

Calculate the mean percentage error. This metric is in relative units. It can be used as a measure of the estimate's bias.

Note that if any truth values are 0, a value of: -Inf (estimate > 0), Inf (estimate < 0), or NaN (estimate == 0) is returned for mpe().

mpe(data, ...)

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

mpe_vec(truth, estimate, na_rm = TRUE, case_weights = NULL, ...)

Arguments

• data: A data.frame containing the columns specified by the truth

  and estimate arguments.

• ...: 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.

• case_weights: The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in data. For _vec() functions, a numeric vector, hardhat::importance_weights(), or hardhat::frequency_weights().

Returns

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 mpe_vec(), a single numeric value (or NA).

Examples

# `solubility_test$solubility` has zero values with corresponding
# `$prediction` values that are negative. By definition, this causes `Inf`
# to be returned from `mpe()`.
solubility_test[solubility_test$solubility == 0, ]

mpe(solubility_test, solubility, prediction)

# We'll remove the zero values for demonstration
solubility_test <- solubility_test[solubility_test$solubility != 0, ]

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

library(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) %>%
  mpe(solubility, prediction)

metric_results

# Resampled mean estimate
metric_results %>%
  summarise(avg_estimate = mean(.estimate))

See Also

Other numeric metrics: ccc(), huber_loss(), huber_loss_pseudo(), iic(), mae(), mape(), mase(), msd(), poisson_log_loss(), rmse(), rpd(), rpiq(), rsq(), rsq_trad(), smape()

Other accuracy metrics: ccc(), huber_loss(), huber_loss_pseudo(), iic(), mae(), mape(), mase(), msd(), poisson_log_loss(), rmse(), smape()

Author(s)

Thomas Bierhance