Anomalies according to Peirce's and Chauvenet's criteria

Peirce's criterion and Chauvenet's criterion were both proposed in the 1800s as a way of determining what observations should be rejected in a univariate sample.

Usage

peirce_anomalies(y)

chauvenet_anomalies(y)

Arguments

y

numerical vector of observations

Value

A logical vector

Details

These functions take a univariate sample y and return a logical vector indicating which observations should be considered anomalies according to either Peirce's criterion or Chauvenet's criterion.

References

Peirce, B (1852). Criterion for the rejection of doubtful observations. The Astronomical Journal, 2(21), 161–163.

Chauvenet, W (1863). 'Method of least squares'. Appendix to Manual of Spherical and Practical Astronomy, Vol.2, Lippincott, Philadelphia, pp.469-566.

Hyndman, R J (2026) "That's weird: Anomaly detection using R", Section 4.3, https://OTexts.com/weird/.

Author

Rob J Hyndman

Examples

y <- rnorm(1000)
tibble(y = y) |> filter(peirce_anomalies(y))
#> # A tibble: 0 × 1
#> # ℹ 1 variable: y <dbl>
tibble(y = y) |> filter(chauvenet_anomalies(y))
#> # A tibble: 0 × 1
#> # ℹ 1 variable: y <dbl>