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.

Rob J Hyndman (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: 1 × 1
#>       y
#>   <dbl>
#> 1  3.81
tibble(y = y) |> filter(chauvenet_anomalies(y))
#> # A tibble: 1 × 1
#>       y
#>   <dbl>
#> 1  3.81