easyCODA-package {easyCODA}

Compositional Data Analysis in Practice

Description

Univariate and multivariate methods for compositional data analysis, based on logratios. The package implements the approach in the book Compositional Data Analysis in Practice by Michael Greenacre (2018), where accent is given to simple pairwise logratios. Selection can be made of logratios that account for a maximum percentage of logratio variance. Various multivariate analyses of logratios are included in the package.

Details

The DESCRIPTION file:

Package:	easyCODA
Type:	Package
Version:	0.34.3
Date:	2020-10-17
Depends:	ca (>= 0.7), vegan (>= 2.3), ellipse (>= 0.4.1)
Title:	Compositional Data Analysis in Practice
Author:	Michael Greenacre
Maintainer:	Michael Greenacre <[e-mail]>
Description:	Univariate and multivariate methods for compositional data analysis, based on logratios. The package implements the approach in the book Compositional Data Analysis in Practice by Michael Greenacre (2018), where accent is given to simple pairwise logratios. Selection can be made of logratios that account for a maximum percentage of logratio variance. Various multivariate analyses of logratios are included in the package.
License:	GPL
URL:	https://github.com/michaelgreenacre/CODAinPractice/
Repository:	R-Forge
Repository/R-Forge/Project:	easycoda
Repository/R-Forge/Revision:	39
Repository/R-Forge/DateTimeStamp:	2020-09-17 10:54:11
Date/Publication:	2020-09-17 10:54:11

Index of help topics:

ACLUST                  Amalgamation clustering of the parts of a
                        compositional data matrix
ALR                     Additive logratios
BAR                     Compositional bar plot
CA                      Correspondence analysis
CIplot_biv              Bivariate confidence and data ellipses
CLOSE                   Closure of rows of compositional data matrix
CLR                     Centred logratios
DOT                     Dot plot
DUMMY                   Dummy variable (indicator) coding
ILR                     Isometric logratio
LR                      All pairwise logratios
LR.VAR                  Total logratio variance
LRA                     Logratio analysis
PCA                     Principal component analysis
PLOT.CA                 Plot the results of a correspondence analysis
PLOT.LRA                Plot the results of a logratio analysis
PLOT.PCA                Plot the results of a principal component
                        analysis
PLOT.RDA                Plot the results of a redundancy analysis
PLR                     Pivot logratios
RDA                     Redundancy analysis
SLR                     Amalgamation (summed) logratio
STEP                    Stepwise selection of logratios
VAR                     Variance of a vector of observations, dividing
                        by n rather than n-1
WARD                    Ward clustering of a compositional data matrix
cups                    Dataset: RomanCups
easyCODA-package        Compositional Data Analysis in Practice
fish                    Dataset: FishMorphology
invALR                  Inverse of additive logratios
invCLR                  Inverse of centred logratios
invSLR                  Inverse of full set of amalgamation balances
time                    Dataset: TimeBudget
veg                     Dataset: Vegetables

Author(s)

Michael Greenacre

Maintainer: Michael Greenacre <[e-mail]>

References

Greenacre, Michael (2018) Compositional Data Analysis in Practice. Chapman & Hall / CRC Press

See Also

ca

Examples

# Roman cups glass compositions
data(cups)
# unweighted logratio analysis
cups.uLRA <- LRA(cups, weight=FALSE)
PLOT.LRA(cups.uLRA)
# weighted logratio analysis
cups.wLRA <- LRA(cups)
PLOT.LRA(cups.wLRA)
# author data set from the ca package
data(author)
which(author == 0, arr.ind = TRUE)
# row 5 (Farewell to Arms) and col 17 (Q) has a zero
# replace it with 0.5 for the logratio analysis
author[5,17] <- 0.5
# LRA (weighted by default)
# Here the ca plot function plot.ca is used
plot(LRA(author))