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 data.frame
mpe(data, truth, estimate, na_rm = TRUE, ...)

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

See also

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

Other accuracy metrics: ccc(), huber_loss_pseudo(), huber_loss(), iic(), mae(), mape(), mase(), 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$solubility == 0,]
#> solubility prediction #> 17 0 -0.1532030 #> 220 0 -0.3876578
mpe(solubility_test, solubility, prediction)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 mpe standard Inf
# We'll remove the zero values for demonstration solubility_test <- solubility_test[solubility_test$solubility != 0,] # Supply truth and predictions as bare column names mpe(solubility_test, solubility, prediction)
#> # A tibble: 1 x 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 x 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 x 1 #> avg_estimate #> <dbl> #> 1 -7.38