Ratio of performance to inter-quartile

These functions are appropriate for cases where the model outcome is a numeric. The ratio of performance to deviation (rpd()) and the ratio of performance to inter-quartile (rpiq()) are both measures of consistency/correlation between observed and predicted values (and not of accuracy).

Usage

rpiq(data, ...)

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

rpiq_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 rpd_vec(), a single numeric value (or NA).

Details

RPIQ is a metric that should be maximized. The output ranges from 0 to ∞, with higher values indicating better model performance.

The formula for RPIQ is:

$$\text{RPIQ} = \frac{\text{IQR}(\text{truth})}{\text{RMSE}}$$

References

Williams, P.C. (1987) Variables affecting near-infrared reflectance spectroscopic analysis. In: Near Infrared Technology in the Agriculture and Food Industries. 1st Ed. P.Williams and K.Norris, Eds. Am. Cereal Assoc. Cereal Chem., St. Paul, MN.

Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger, J.M. and McBratney, A., (2010). Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. TrAC Trends in Analytical Chemistry, 29(9), pp.1073-1081.

See also

All numeric metrics

The closely related deviation metric: rpd()

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

Other consistency metrics: ccc(), rpd(), rsq(), rsq_trad()

Author

Pierre Roudier

Examples

# Supply truth and predictions as bare column names
rpd(solubility_test, solubility, prediction)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 rpd     standard        2.88

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) |>
  rpd(solubility, prediction)

metric_results
#> # A tibble: 10 × 4
#>    resample .metric .estimator .estimate
#>    <chr>    <chr>   <chr>          <dbl>
#>  1 1        rpd     standard        2.78
#>  2 10       rpd     standard        2.87
#>  3 2        rpd     standard        3.04
#>  4 3        rpd     standard        3.41
#>  5 4        rpd     standard        3.02
#>  6 5        rpd     standard        2.66
#>  7 6        rpd     standard        2.81
#>  8 7        rpd     standard        2.61
#>  9 8        rpd     standard        3.45
#> 10 9        rpd     standard        2.93

# Resampled mean estimate
metric_results |>
  summarise(avg_estimate = mean(.estimate))
#> # A tibble: 1 × 1
#>   avg_estimate
#>          <dbl>
#> 1         2.96