Compositional Data Analysis in Practice

Documentation for package ‘easyCODA’ version 0.34.3

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