OutlierDetection (version 0.1.1)

maha: Outlier detection using Mahalanobis Distance

Description

Takes a dataset and finds its outliers using modelbased method

Usage

maha(x, cutoff = 0.95, rnames = FALSE)

Arguments

x

dataset for which outliers are to be found

cutoff

Percentile threshold used for distance, default value is 0.95

rnames

Logical value indicating whether the dataset has rownames, default value is False

Value

Outlier Observations: A matrix of outlier observations

Location of Outlier: vector of Sr. no. of outliers

Outlier probability: vector of (1-p value) of outlier observations

Details

maha computes Mahalanibis distance an observation and based on the Chi square cutoff, labels an observation as outlier. Outlierliness of the labelled 'Outlier' is also reported based on its p values. For bivariate data, it also shows the scatterplot of the data with labelled outliers.

References

Barnett, V. 1978. The study of outliers: purpose and model. Applied Statistics, 27(3), 242<U+2013>250.

Examples

# NOT RUN {
#Create dataset
X=iris[,1:4]
#Outlier detection
maha(X,cutoff=0.9)
# }