EPPlabOutlier: Function to Find Outliers for an epplab Object
In REPPlab: R Interface to 'EPP-Lab', a Java Program for Exploratory Projection Pursuit

Description Usage Arguments Details Value Author(s) References See Also Examples

Function to decide wether observations are considered outliers or not in specific projection directions of an epplab object.

1 2	EPPlabOutlier(x, which = 1:ncol(x$PPdir), k = 3, location = mean, scale = sd)

`x`	An object of class `epplab`.
`which`	The directions in which outliers should be searched. The default is to look at all.
`k`	Numeric value to decide when an observation is considered an outlier or not. Default is 3. See details.
`location`	A function which gives the univariate location as an output. The default is `mean`.
`scale`	A function which gives the univariate scale as an output. The default is `sd`.

Denote location_j as the location of the jth projection direction and analogously scale_j as its scale. Then an observation x is an outlier in the jth projection direction, if |x-location_j| >= k scale_j.

Naturally it is best to use for this purpose robust location and scale measures like median and mad for example.

A list with class 'epplabOutlier' containing the following components:

`outlier`	A matrix with only zeros and ones. A value of 1 classifies the observation as an outlier in this projection direction.
`k`	The factor `k` used.
`location`	The name of the `location` estimator used.
`scale`	The name of the `scale` estimator used.
`PPindex`	The name of the `PPindex` used.
`PPalg`	The name of the `PPalg` used.

Klaus Nordhausen

Ruiz-Gazen, A., Larabi Marie-Sainte, S. and Berro, A. (2010), Detecting multivariate outliers using projection pursuit with particle swarm optimization, COMPSTAT2010, pp. 89-98.

EPPlab

# creating data with 3 outliers
n <-300 
p <- 10
X <- matrix(rnorm(n*p),ncol=p)
X[1,1] <- 9
X[2,4] <- 7 
X[3,6] <- 8
# giving the data rownames, obs.1, obs.2 and obs.3 are the outliers.
rownames(X) <- paste("obs",1:n,sep=".")

PP<-EPPlab(X,PPalg="PSO",PPindex="KurtosisMax",n.simu=20, maxiter=20)
OUT<-EPPlabOutlier(PP, k = 3, location = median, scale = mad)
OUT