yardstick: rsq_trad – R documentation

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rsq_trad

R squared - traditional

Description

Calculate the coefficient of determination using the traditional definition of R squared using sum of squares. For a measure of R squared that is strictly between (0, 1), see rsq().

Usage

rsq_trad(data, ...)

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

rsq_trad_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.

Details

The two estimates for the coefficient of determination, rsq() and rsq_trad(), differ by their formula. The former guarantees a value on (0, 1) while the latter can generate inaccurate values when the model is non-informative (see the examples). Both are measures of consistency/correlation and not of accuracy.

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

Author(s)

Max Kuhn

References

Kvalseth. Cautionary note about R^2. American Statistician (1985) vol. 39 (4) pp. 279-285.

Examples

# Supply truth and predictions as bare column names
rsq_trad(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) %>%
  rsq_trad(solubility, prediction)

metric_results

# Resampled mean estimate
metric_results %>%
  summarise(avg_estimate = mean(.estimate))
# With uninformitive data, the traditional version of R^2 can return
# negative values.
set.seed(2291)
solubility_test$randomized <- sample(solubility_test$prediction)
rsq(solubility_test, solubility, randomized)
rsq_trad(solubility_test, solubility, randomized)

yardstick

Tidy Characterizations of Model Performance

v0.0.8

MIT + file LICENSE

Authors

Max Kuhn [aut], Davis Vaughan [aut, cre], RStudio [cph]

Initial release