Compositional Data Analysis in Practice

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Documentation for package ‘easyCODA’ version 0.34.3

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easyCODA-package Compositional Data Analysis in Practice
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
cups Dataset: RomanCups
DOT Dot plot
DUMMY Dummy variable (indicator) coding
fish Dataset: FishMorphology
ILR Isometric logratio
invALR Inverse of additive logratios
invCLR Inverse of centred logratios
invSLR Inverse of full set of amalgamation balances
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
time Dataset: TimeBudget
VAR Variance of a vector of observations, dividing by n rather than n-1
veg Dataset: Vegetables
WARD Ward clustering of a compositional data matrix