<p>It is a small data set to use as a demo for the SCOUTer package. It consists of
normally distributed variables, with two Principal Components explaining an
80% of the total variance.</p>

datasets

Using principal component analysis as a base model, 'SCOUTer'
offers a new approach to simulate outliers in a simple and precise way.
The user can generate new observations defining them by a pair of well-known
statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2)
statistics. Just by introducing the target values of the SPE and T^2, 'SCOUTer'
returns a new set of observations with the desired target properties.
Authors: Alba Gonz<c3><a1>lez, Abel Folch-Fortuny, Francisco Arteaga and
Alberto Ferrer (2020).

Alba Gonzalez Cebrian

SCOUTer

Simulate Controlled Outliers

Abel Folch-Fortuny

Francisco Arteaga

Alberto Ferrer

X function

<p>A matrix data frame with 50 rows and 5 normally distributed variables.</p>

X: Demo dataset

Description

Usage

Arguments

Format