| 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
 [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2022-08-25 22:52:33 UTC |
dobin: Dimension Reduction for Outlier Detection
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.
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 |
norm |
The normalization technique. Default is Min-Max, which normalizes each column to values between 0 and 1. |
k |
Parameter |
Value
A list with the following components:
rotation |
The basis vectors suitable for outlier detection. |
coords |
The dobin coordinates of the data |
Yspace |
The The associated |
Ypairs |
The pairs in |
zerosdcols |
Columns in |
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