Description Details Author(s) References See Also Examples
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:
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Michael Greenacre
Maintainer: Michael Greenacre <michael.greenacre@upf.edu>
Greenacre, Michael (2018) Compositional Data Analysis in Practice. Chapman & Hall / CRC Press
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # 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))
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Loading required package: ca
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-7
Loading required package: ellipse
Attaching package: ‘ellipse’
The following object is masked from ‘package:graphics’:
pairs
row col
farewell to arms (hemingway) 5 17
Warning: The row sums of this matrix are not constant, so this may not be a compositional data matrix
Nevertheless, the method is still valid and continues since all the data values are positive
If this was not intended, please supply a valid compositional table with constant row sums
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