moutlier_lof: LOF outlier detection

In mkoohafkan/wqptools: Outlier Detection Working Group Toolbox

Description

Performs multivariate outlier detection using Local Outlier Factor algorithm.

Usage

moutlier_lof(
  xs,
  mask = !Reduce("|", lapply(xs, is.na)),
  threshold = c(1.5, 2),
  return.score = FALSE,
  ...
)

Arguments

xs	A dataframe or list of vectors (which will be coerced to a numeric matrix).
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).
...	Additional arguments to dbscan::lof, namely k.

Details

the values of threshold identify mild and extreme outliers based on the LOF score. Scores significantly larger than 1 indicate outliers. Default values are 1.5 for "mild" outliers and 2.0 for "extreme" outliers.

Examples

x = seq(0, 34, by = 0.25)*pi
noise = rlnorm(length(x), meanlog = 1, sdlog = 3)
y=sin(x) + noise
mask = noise < 1

if (requireNamespace("dbscan", quietly = TRUE)) {
  moutlier_lof(list(y))
  moutlier_lof(list(x, y), mask)
  moutlier_lof(list(x, y), mask, threshold = c(1, 2))
  moutlier_lof(list(x, y), return.score = TRUE)
}

mkoohafkan/wqptools documentation built on May 2, 2021, 8:12 p.m.