print.outCoDa

Gives a short information of the amount of outliers in objects of class <squote>outCoDa</squote>.

print

Methods for analysis of compositional data
    including robust methods, imputation, methods to replace rounded zeros, (robust) 
    outlier detection for compositional data, (robust) principal component analysis for
    compositional data, (robust) factor analysis for compositional data,
    (robust) discriminant analysis for compositional data (Fisher rule), robust
    regression with compositional predictors and (robust) Anderson-Darling
    normality tests for compositional data as well as popular log-ratio
    transformations (addLR, cenLR, isomLR, and their inverse transformations).
    In addition, visualisation and diagnostic tools are implemented as well as
    high and low-level plot functions for the ternary diagram.

print.outCoDa: print method for outCoDa objects

Description

Usage

Arguments

See Also

Examples