outlier_tscore: t-score test for outliers
In SuisunMarshBranch/wqptools: Outlier Detection Working Group Toolbox

Description Usage Arguments Details Examples

Performs a t-score test for outliers.

outlier_tscore(
  x,
  mask = !is.na(x),
  threshold = c(0.9, 0.95),
  return.score = FALSE
)

`x`	A vector of data.
`mask`	A logical vector that defines which values in `x` will used when computing statistics. Useful when a subset of quality-assured data is available. Default mask is non-`NA` Values.
`threshold`	A length-two vector identifying thresholds for "mild" and "extreme" outliers.
`return.score`	if `TRUE`, return the numeric outlier score. If `FALSE`, return an ordered factor classifying the observations as one of "not outlier" (1), "mild outlier" (2), or "extreme outlier" (3).

The values of threshold identify the quantiles of the t-distribution used to identify mild and extreme outliers. Default values are 0.9 for "mild" outliers and 0.95 for "extreme" outliers.

The t-score is equivalent to the z-score for sample sizes greater than 30.

x = seq(0, 34, by = 0.25)*pi
noise = rnorm(length(x), mean = 1, sd = 3)
y = sin(x) + noise
mask = noise < 1

outlier_tscore(y)
outlier_tscore(y, mask)
outlier_tscore(y, mask, threshold = c(0.8, 0.9))
outlier_tscore(y, return.score = TRUE)