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
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.
Examples
y <- rnorm(1000)
tibble(y = y) |> filter(peirce_anomalies(y))
#> # A tibble: 1 × 1
#> y
#> <dbl>
#> 1 -3.83
tibble(y = y) |> filter(chauvenet_anomalies(y))
#> # A tibble: 1 × 1
#> y
#> <dbl>
#> 1 -3.83