Compositional Data Analysis in Practice
Amalgamation clustering of the parts of a compositional data matrix
Additive logratios
Compositional bar plot
Correspondence analysis
Bivariate confidence and data ellipses
Closure of rows of compositional data matrix
Centred logratios
Dot plot
Dummy variable (indicator) coding
tools:::Rd_package_title("easyCODA")
Find the best ALR transformation
Isometric logratio
Inverse of additive logratios
Inverse of centred logratios
Inverse of full set of amalgamation balances
All pairwise logratios
Total logratio variance
Logratio analysis
Principal component analysis
Plot the results of a correspondence analysis
Plot the results of a logratio analysis
Plot the results of a principal component analysis
Plot the results of a redundancy analysis
Pivot logratios
Redundancy analysis
Amalgamation (summed) logratio
Stepwise selection of logratios
Stepwise selection of pairwise logratios for generalized linear modell...
Variance of a vector of observations, dividing by n rather than n-1
Ward clustering of a compositional data matrix
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.