remove.outliers: Remove outliers

In odetector: Outlier Detection Using Partitioning Clustering Algorithms

Remove outliers

Description

Removes the detected outliers from a data set.

Usage

  remove.outliers(x, ot=1, sc=FALSE)

Arguments

x	an object of outliers class containing the outliers to be removed.
ot	an integer specifying the outlier detection approach. The default is 1 for the Approach 1. For the other methods use 2 or 3. See detect.outliers for the details.
sc	a logical value for including the objects in the small clusters into removal process. The default is FALSE. Use TRUE for removing the objects in the small clusters.

Value

Xr	a numeric matrix containing the outliers-removed data set.

Author(s)

Zeynel Cebeci, Cagatay Cebeci, Yalcin Tahtali

See Also

detect.outliers, pairs.outliers, plot.outliers, print.outliers, summary.outliers

Examples

# Load the dataset x3p4c and extract the first three columns to x
data(x3p4c)
x <- x3p4c[,1:3]

# For 4 clusters, run Unsupervised Possibilistic Fuzzy C-Means (UPFC) algorithm 
# of the package ppclust 
res.upfc <- ppclust::upfc(x, centers=4)

# Detect the outliers 
out <- detect.outliers(res.upfc)
 
# Remove the outliers 
Xr1 <- remove.outliers(out, ot=1)
print(Xr1)

# Remove the outliers including the collective outliers 
Xr2 <- remove.outliers(out, ot=1, sc=TRUE)
print(Xr2)

odetector documentation built on Nov. 10, 2022, 5:45 p.m.