easyCODA-package {easyCODA} | R Documentation |
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.
The DESCRIPTION file:
Package: | easyCODA |
Type: | Package |
Version: | 0.31.2 |
Date: | 2019-03-10 |
Depends: | ca (>= 0.6), vegan (>= 2.3), ellipse (>= 0.4.1) |
Title: | Compositional Data Analysis in Practice |
Author: | Michael Greenacre |
Maintainer: | Michael Greenacre <michael.greenacre@upf.edu> |
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: | 28 |
Repository/R-Forge/DateTimeStamp: | 2019-05-16 12:51:21 |
Date/Publication: | 2019-05-16 12:51:21 |
Index of help topics:
ALR Additive logratios BAR Compositional bar plot CA Correspondence analysis 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 cups Dataset: RomanCups easyCODA-package Compositional Data Analysis in Practice fish Dataset: FishMorphology invALR Inverse of additive logratios invCLR Inverse of centred logratios time Dataset: TimeBudget veg Dataset: Vegetables
Michael Greenacre
Maintainer: Michael Greenacre <michael.greenacre@upf.edu>
Greenacre, Michael (2018) Compositional Data Analysis in Practice. Chapman & Hall / CRC Press
# 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))