R: High dimensional MCD based detection of outliers

High dimensional MCD based detection of outliers {Rfast} | R Documentation

## High dimensional MCD based detection of outliers

### Description

High dimensional MCD based detection of outliers.

### Usage

    rmdp(y, alpha = 0.05, itertime = 100)

### Arguments

`y` | A matrix with numerical data with more columns (p) than rows (n), i.e. n<p.
`alpha` | The significance level, i.e. used to decide whether an observation is said to be considered a possible outlier. The default value is 0.05.
`itertime` | The number of iterations the algorithm will be ran. The higher the sample size, the larger this number must be. With 50 observations in R^1000 maybe this has to be 1000 in order to produce stable results.

### Details

High dimensional outliers (n<<p) are detected using a properly constructed MCD. The variances of the variables are used and the determinant is simply their product.

### Value

A list including: runtime = runtime, dis = dis, wei = wei

`runtime` | The duration of the process.
`dis` | The final estimated Mahalanobis type normalised distances.
`wei` | A bollean variable vector specifying whether an observation is "clean" (TRUE) or a possible outlier (FALSE).
`cova` | The estimated covatriance matrix.

### Author(s)

Initial R code: Changliang Zou <nk.chlzou@gmail.com> R code modifications: Michail Tsagris <mtsagris@uoc.gr> C++ implementation: Manos Papadakis <papadakm95@gmail.com> Documentation: Michail Tsagris <mtsagris@uoc.gr> and Changliang Zhou <nk.chlzou@gmail.com>

### References

Ro K., Zou C., Wang Z. and Yin G. (2015). Outlier detection for high-dimensional data. Biometrika, 102(3):589-599.

### See Also

`colmeans, colVars, colMedians`

### Examples

    x <- matrix(rnorm(50 * 400), ncol = 400)
    a <- rmdp(x, itertime = 500)

    x<-a<-NULL

[Package Rfast version 2.0.4 Index]