knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
Welcome to the balance
GitHub page!
Balances have become a cornerstone of compositional data analysis. However, conceptualizing balances is difficult, especially for high-dimensional data. Most often, investigators visualize balances with "balance dendrograms". However, this visualization tool does not scale well for large data. This package provides an alternative scheme for visualizing balances. This package also provides a method for principal balance analysis.
library(devtools) devtools::install_github("tpq/balance") library(balance) ?balance
library(balance)
We will demonstrate this package using example data taken from the robCompositions
package. The "expenditures" matrix contains 20 compositions (row), each measuring 5 components (columns). As compositional data, the abundances are irrelevant and each composition sums to unity. The "y1" matrix is a serial binary partition (SBP) matrix that describes how to partition the 5 components into 4 balances.
data(expenditures) y1 <- data.frame(c(1, 1, 1, -1, -1), c(1, -1, -1, 0, 0), c(0, +1, -1, 0, 0), c(0, 0, 0, +1, -1)) colnames(y1) <- paste0("z", 1:4)
With the data loaded, we can calculate and visualize the balances.
res <- balance.plot(expenditures, y1, size.text = 8)
Optionally, users can color components (in left figure) or samples (in right figure) based on a user-defined grouping. To do this, users must provide a vector of group labels for each component via the d.group
argument (or for each sample via the n.group
argument). Here, we color components and samples by user-defined groupings.
res <- balance.plot(expenditures, y1, d.group = c("A", "B", "A", "B", "C"), n.group = c(rep("A", 10), rep("B", 10)), size.text = 8)
To learn more about balance
, please see the vignette and relevant literature.
citation("balance")
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