bss.background

Coordinates of the BSS data background map

datasets

Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files.

P. Filzmoser

mvoutlier

Multivariate Outlier Detection Based on Robust Methods

bss.background function

A data frame with 6093 observations on the following 2 variables.<dl class="dl-horizontal">
 <dt><code>V1</code></dt><dd>a numeric vector with the x-coordinates</dd>
 <dt><code>V2</code></dt><dd>a numeric vector with the y-coordinates</dd>
 </dl>

Format

Background map for the BSS project — bss.background

A data frame with 6093 observations on the following 2 variables.<dl class='dl-horizontal'>
 <dt><code>V1</code></dt><dd>a numeric vector with the x-coordinates</dd>
 <dt><code>V2</code></dt><dd>a numeric vector with the y-coordinates</dd>
 </dl>

bss.background: Background map for the BSS project

Description

Usage

Arguments

Format

Details

References

Examples