Type: Package
Title: Dimension Reduction for Outlier Detection
Version: 1.0.4
Maintainer: Sevvandi Kandanaarachchi <sevvandik@gmail.com>
Description: A dimension reduction technique for outlier detection. DOBIN: a Distance based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier detection. This is not an outlier detection method; rather it is a pre-processing method for outlier detection. It brings outliers to the fore-front using fewer basis vectors (Kandanaarachchi, Hyndman 2020) <doi:10.1080/10618600.2020.1807353>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dbscan, ggplot2, pracma
RoxygenNote: 7.2.1
Suggests: knitr, rmarkdown, OutliersO3, FNN
VignetteBuilder: knitr
Depends: R (≥ 3.4.0)
URL: https://sevvandi.github.io/dobin/
NeedsCompilation: no
Packaged: 2022-08-25 22:03:32 UTC; kan092
Author: Sevvandi Kandanaarachchi ORCID iD [aut, cre]
Repository: CRAN
Date/Publication: 2022-08-25 22:52:33 UTC

dobin: Dimension Reduction for Outlier Detection

Description

logo

A dimension reduction technique for outlier detection. DOBIN: a Distance based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier detection. This is not an outlier detection method; rather it is a pre-processing method for outlier detection. It brings outliers to the fore-front using fewer basis vectors (Kandanaarachchi, Hyndman 2020) doi:10.1080/10618600.2020.1807353.

Author(s)

Maintainer: Sevvandi Kandanaarachchi sevvandik@gmail.com (ORCID)

See Also

Useful links:

Plots the first two components of the dobin space.

Description

Scatterplot of the first two columns in the dobin space.

Usage

## S3 method for class 'dobin'
autoplot(object, ...)

Arguments

object

The output of the function 'dobin'.

...

Other arguments currently ignored.

Value

A ggplot object.

Examples

X <- rbind(
  data.frame(x = rnorm(500),
             y = rnorm(500),
             z = rnorm(500)),
  data.frame(x = rnorm(5, mean = 10, sd = 0.2),
             y = rnorm(5, mean = 10, sd = 0.2),
             z = rnorm(5, mean = 10, sd = 0.2))
)
dob <- dobin(X)
autoplot(dob)

Computes a set of basis vectors for outlier detection.

Description

This function computes a set of basis vectors suitable for outlier detection.

Usage

dobin(xx, frac = 0.95, norm = 1, k = NULL)

Arguments

xx

The input data in a dataframe, matrix or tibble format.

frac

The cut-off quantile for Y space. Default is 0.95.

norm

The normalization technique. Default is Min-Max, which normalizes each column to values between 0 and 1. norm = 0 skips normalization. Other values of norm defaults to Median-IQR normalization.

k

Parameter k for k nearest neighbours with a default value of 5% of the number of observations with a cap of 20.

Value

A list with the following components:

rotation

The basis vectors suitable for outlier detection.

coords

The dobin coordinates of the data xx.

Yspace

The The associated Y space.

Ypairs

The pairs in xx used to construct the Y space.

zerosdcols

Columns in xx with zero standard deviation. This is computed only if the number of columns are greater than the number of rows.

Examples

# A bimodal distribution in six dimensions, with 5 outliers in the middle.
set.seed(1)
x2 <- rnorm(405)
x3 <- rnorm(405)
x4 <- rnorm(405)
x5 <- rnorm(405)
x6 <- rnorm(405)
x1_1 <- rnorm(mean = 5, 400)
mu2 <-  0
x1_2 <- rnorm(5, mean=mu2, sd=0.2)
x1 <- c(x1_1, x1_2)
X1 <- cbind(x1,x2,x3,x4,x5,x6)
X2 <- cbind(-1*x1_1,x2[1:400],x3[1:400],x4[1:400],x5[1:400],x6[1:400])
X <- rbind(X1, X2)
labs <- c(rep(0,400), rep(1,5), rep(0,400))
dob <- dobin(X)
autoplot(dob)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

ggplot2

autoplot