LOF {bigutilsr} R Documentation

## Local Outlier Factor (LOF)

### 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.

`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"))

```

[Package bigutilsr version 0.3.4 Index]