maxlof: Detection of multivariate outliers using the LOF algorithm
In dprep: Data Pre-Processing and Visualization Functions for Classification

Description Usage Arguments Details Value Author(s) References Examples

A function that detects multivariate outliers using the local outlier factor for a matrix over a range of neighbors called minpts.

1	maxlof(data, name = "", minptsl = 10, minptsu = 20)

`data`	Dataset for outlier detection
`name`	Name of dataset used in the graph title.
`minptsl`	Lower bound for the number of neighbors
`minptsu`	Upper bound for the number of neighbors

Calls on the function "lofactor" to compute the local outlier factor for each integer number of neighbors in the range [minptsl, minptsu]. Also displays a plot of the factors for each observation of the dataset. In the plot, the user should seek to identify observations with large gaps between outlyingness measures. These would be candidates for outliers.

maxlofactor

A vector containing the index of each observation of the dataset and the corresponding local outlier factor.

Caroline Rodriguez

Breuning, M., Kriegel, H., Ng, R.T, and Sander. J. (2000). LOF: Identifying density-based local outliers. In Proceedings of the ACM SIGMOD International Conference on Management of Data.

## Not run: #Detecting top 10 outliers in class number 1 of Breastw using the LOF algorithm
data(breastw)
breastw=ce.impute(breastw,"median",1:9)
breastw1.lof=maxlof(breastw[breastw[,10]==1,],name="Breast-Wisconsin",30,40)
breastw1.lof[order(breastw1.lof,decreasing=TRUE)][1:10]

## End(Not run)