robCompositions: ternaryDiag – R documentation

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ternaryDiag

Ternary diagram

Description

This plot shows the relative proportions of three variables (compositional parts) in one diagramm. Before plotting, the data are scaled.

Usage

ternaryDiag(
  x,
  name = colnames(x),
  text = NULL,
  grid = TRUE,
  gridCol = grey(0.6),
  mcex = 1.2,
  line = "none",
  robust = TRUE,
  group = NULL,
  tol = 0.975,
  ...
)

Arguments

`x`	matrix or data.frame with 3 columns
`name`	names of the variables
`text`	default NULL, text for each point can be provided
`grid`	if TRUE a grid is plotted additionally in the ternary diagram
`gridCol`	color for the grid lines
`mcex`	label size
`line`	may be set to “none”, “pca”, “regression”, “regressionconf”, “regressionpred”, “ellipse”, “lda”
`robust`	if line equals TRUE, it dedicates if a robust estimation is applied or not.
`group`	if line equals “da”, it determines the grouping variable
`tol`	if line equals “ellipse”, it determines the parameter for the tolerance ellipse
`...`	further parameters, see, e.g., `par()`

Details

The relative proportions of each variable are plotted.

Author(s)

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>, Matthias Templ

References

Reimann, C., Filzmoser, P., Garrett, R.G., Dutter, R. (2008) Statistical Data Analysis Explained. Applied Environmental Statistics with R. John Wiley and Sons, Chichester.

Examples

data(arcticLake)
ternaryDiag(arcticLake)

data(coffee)
x <- coffee[,2:4]
grp <- as.integer(coffee[,1])
ternaryDiag(x, col=grp, pch=grp)
ternaryDiag(x, grid=FALSE, col=grp, pch=grp)
legend("topright", legend=unique(coffee[,4]), pch=1:2, col=1:2)

ternaryDiag(x, grid=FALSE, col=grp, pch=grp, line="ellipse", tol=c(0.975,0.9), lty=2)
ternaryDiag(x, grid=FALSE, line="pca")
ternaryDiag(x, grid=FALSE, col=grp, pch=grp, line="pca", lty=2, lwd=2)

robCompositions

Compositional Data Analysis

v2.3.0

GPL (>= 2)

Authors

Matthias Templ [aut, cre] (<https://orcid.org/0000-0002-8638-5276>), Karel Hron [aut] (<https://orcid.org/0000-0002-1847-6598>), Peter Filzmoser [aut] (<https://orcid.org/0000-0002-8014-4682>), Kamila Facevicova [ctb], Petra Kynclova [ctb], Jan Walach [ctb], Veronika Pintar [ctb], Jiajia Chen [ctb], Dominika Miksova [ctb], Bernhard Meindl [ctb], Alessandra Menafoglio [ctb] (<https://orcid.org/0000-0003-0682-6412>), Alessia Di Blasi [ctb], Federico Pavone [ctb], Gianluca Zeni [ctb]

Initial release

2020-11-18