# lof

##### Local Outlier Factor Score

Calculate the Local Outlier Factor (LOF) score for each data point using a kd-tree to speed up kNN search.

- Keywords
- model

##### Usage

`lof(x, k = 4, ...)`

##### Arguments

- x
a data matrix or a dist object.

- k
size of the neighborhood.

- …
further arguments are passed on to

`kNN`

.

##### Details

LOF compares the local density of an point to the local densities of its neighbors. Points that have a substantially lower density than their neighbors are considered outliers. A LOF score of approximately 1 indicates that density around the point is comparable to its neighbors. Scores significantly larger than 1 indicate outliers.

Note: If there are more than `k`

duplicate points in the data, then lof
can become `NaN`

caused by an infinite local density.
In this case we set lof to 1. The paper by Breunig et al (2000) suggests a different method of removing all duplicate points first.

##### Value

A numeric vector of length `ncol(x)`

containing LOF values for
all data points.

##### References

Breunig, M., Kriegel, H., Ng, R., and Sander, J. (2000). LOF: identifying
density-based local outliers. In *ACM Int. Conf. on Management of Data,*
pages 93-104.

##### See Also

`kNN`

, `pointdensity`

, `glosh`

.

##### Examples

```
# NOT RUN {
set.seed(665544)
n <- 100
x <- cbind(
x=runif(10, 0, 5) + rnorm(n, sd=0.4),
y=runif(10, 0, 5) + rnorm(n, sd=0.4)
)
### calculate LOF score
lof <- lof(x, k=3)
### distribution of outlier factors
summary(lof)
hist(lof, breaks=10)
### point size is proportional to LOF
plot(x, pch = ".", main = "LOF (k=3)")
points(x, cex = (lof-1)*3, pch = 1, col="red")
text(x[lof>2,], labels = round(lof, 1)[lof>2], pos = 3)
# }
```

*Documentation reproduced from package dbscan, version 1.1-1, License: GPL (>= 2)*