Statistical tests for anomalies using Grubbs' test and Dixon's test

Grubbs' test (proposed in 1950) identifies possible anomalies in univariate data using z-scores assuming the data come from a normal distribution. Dixon's test (also from 1950) compares the difference in the largest two values to the range of the data. Critical values for Dixon's test have been computed using simulation with interpolation using a quadratic model on logit(alpha) and log(log(n)).

Usage

grubbs_anomalies(y, alpha = 0.05)

dixon_anomalies(y, alpha = 0.05, two_sided = TRUE)

Arguments

y

numerical vector of observations

alpha

size of the test.

two_sided

If TRUE, both minimum and maximums will be considered. Otherwise only the maximum will be used. (Take negative values to consider only the minimum with two_sided=FALSE.)

Value

A logical vector

Details

Grubbs' test is based on z-scores, and a point is identified as an anomaly when the associated absolute z-score is greater than a threshold value. A vector of logical values is returned, where TRUE indicates an anomaly. This version of Grubbs' test looks for outliers anywhere in the sample. Grubbs' original test came in several variations which looked for one outlier, or two outliers in one tail, or two outliers on opposite tails. These variations are implemented in the grubbs.test function. Dixon's test only considers the maximum (and possibly the minimum) as potential outliers.

References

Grubbs, F. E. (1950). Sample criteria for testing outlying observations. Annals of Mathematical Statistics, 21(1), 27–58. Dixon, W. J. (1950). Analysis of extreme values. Annals of Mathematical Statistics, 21(4), 488–506.

See also

grubbs.test, dixon.test

Author

Rob J Hyndman

Examples

x <- c(rnorm(1000), 5:10)
tibble(x = x) |> filter(grubbs_anomalies(x))
#> # A tibble: 6 × 1
#>       x
#>   <dbl>
#> 1     5
#> 2     6
#> 3     7
#> 4     8
#> 5     9
#> 6    10
tibble(x = x) |> filter(dixon_anomalies(x))
#> # A tibble: 0 × 1
#> # ℹ 1 variable: x <dbl>
y <- c(rnorm(1000), 5)
tibble(y = y) |> filter(grubbs_anomalies(y))
#> # A tibble: 1 × 1
#>       y
#>   <dbl>
#> 1     5
tibble(y = y) |> filter(dixon_anomalies(y))
#> # A tibble: 1 × 1
#>       y
#>   <dbl>
#> 1     5