LOF: Local Outlier Factor (LOF)

In bigutilsr: Utility Functions for Large-scale Data

Description

LOF: Identifying Density-Based Local Outliers.

Usage

LOF(
  U,
  seq_k = c(4, 10, 30),
  combine = max,
  robMaha = FALSE,
  log = TRUE,
  ncores = 1
)

Arguments

U	A matrix, from which to detect outliers (rows). E.g. PC scores.
seq_k	Sequence of numbers of nearest neighbors to use. If multiple k are provided, this returns the combination of statistics. Default is c(4, 10, 30) and use max to combine (see combine).
combine	How to combine results for multiple k? Default uses max.
robMaha	Whether to use a robust Mahalanobis distance instead of the normal euclidean distance? Default is FALSE, meaning using euclidean.
log	Whether to return the logarithm of LOFs? Default is TRUE.
ncores	Number of cores to use. Default is 1.

References

Breunig, Markus M., et al. "LOF: identifying density-based local outliers." ACM sigmod record. Vol. 29. No. 2. ACM, 2000.

See Also

prob_dist()

Examples

X <- readRDS(system.file("testdata", "three-pops.rds", package = "bigutilsr"))
svd <- svds(scale(X), k = 10)

llof <- LOF(svd$u)
hist(llof, breaks = nclass.scottRob)
tukey_mc_up(llof)

llof_maha <- LOF(svd$u, robMaha = TRUE)
hist(llof_maha, breaks = nclass.scottRob)
tukey_mc_up(llof_maha)

lof <- LOF(svd$u, log = FALSE)
hist(lof, breaks = nclass.scottRob)
str(hist_out(lof))
str(hist_out(lof, nboot = 100))
str(hist_out(lof, nboot = 100, breaks = "FD"))

bigutilsr documentation built on April 14, 2021, 1:06 a.m.