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Mean percentage error

Source: R/num-mpe.R
mpe.Rd

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().

Usage

mpe(data, ...)

# S3 method for 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().

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

See also

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

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

Author

Thomas Bierhance

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, ]
#>     solubility prediction
#> 17           0 -0.1532030
#> 220          0 -0.3876578

mpe(solubility_test, solubility, prediction)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 mpe     standard         Inf

# 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)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 mpe     standard        16.1

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
#> # A tibble: 10 × 4
#>    resample .metric .estimator .estimate
#>    <chr>    <chr>   <chr>          <dbl>
#>  1 1        mpe     standard     -56.2  
#>  2 10       mpe     standard      50.4  
#>  3 2        mpe     standard     -27.9  
#>  4 3        mpe     standard       0.470
#>  5 4        mpe     standard      -0.836
#>  6 5        mpe     standard     -35.3  
#>  7 6        mpe     standard       7.51 
#>  8 7        mpe     standard     -34.5  
#>  9 8        mpe     standard       7.87 
#> 10 9        mpe     standard      14.7  

# Resampled mean estimate
metric_results %>%
  summarise(avg_estimate = mean(.estimate))
#> # A tibble: 1 × 1
#>   avg_estimate
#>          <dbl>
#> 1        -7.38