arrangeC {condvis} | R Documentation |
This function arranges a number of variables in pairs, ordered
by their bivariate relationships. The goal is to discover which variable
pairings are most helpful in avoiding extrapolations when exploring the data
space. Variable pairs with strong bivariate dependencies (not necessarily
linear) are chosen first. The bivariate dependency is measured using
savingby2d
. Each variable appears in the output only once.
arrangeC(data, method = "default")
data |
A dataframe |
method |
The character name for the method to use for measuring
bivariate dependency, passed to |
If data
is so big as to make arrangeC
very slow, a
random sample of rows is used instead. The bivariate dependency measures
are rough, and the ordering algorithm is a simple greedy one, so it is not
worth allowing it too much time. This function exists mainly to provide a
helpful default ordering/pairing for ceplot
.
A list containing character vectors giving variable pairings.
O'Connell M, Hurley CB and Domijan K (2017). “Conditional Visualization for Statistical Models: An Introduction to the condvis Package in R.”Journal of Statistical Software, 81(5), pp. 1-20. <URL:http://dx.doi.org/10.18637/jss.v081.i05>.
data(powerplant)
pairings <- arrangeC(powerplant)
dev.new(height = 2, width = 2 * length(pairings))
par(mfrow = c(1, length(pairings)))
for (i in seq_along(pairings)){
plotxc(powerplant[, pairings[[i]]], powerplant[1, pairings[[i]]],
select.col = NA)
}