yardstick: msd – R documentation

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msd

Mean signed deviation

Description

Mean signed deviation (also known as mean signed difference, or mean signed error) computes the average differences between truth and estimate. A related metric is the mean absolute error (mae()).

Usage

msd(data, ...)

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

msd_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

Mean signed deviation is rarely used, since positive and negative errors cancel each other out. For example, msd_vec(c(100, -100), c(0, 0)) would return a seemingly "perfect" value of 0, even though estimate is wildly different from truth. mae() attempts to remedy this by taking the absolute value of the differences before computing the mean.

This metric is computed as mean(truth - estimate), following the convention that an "error" is computed as observed - predicted. If you expected this metric to be computed as mean(estimate - truth), reverse the sign of the result.

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

Author(s)

Thomas Bierhance

Examples

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

metric_results

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

yardstick

Tidy Characterizations of Model Performance

v0.0.8

MIT + file LICENSE

Authors

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

Initial release

msd