# arrangeC

##### Make a list of variable pairings for condition selecting plots produced by plotxc

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.

##### Usage

`arrangeC(data, method = "default")`

##### Arguments

- data
A dataframe

- method
The character name for the method to use for measuring bivariate dependency, passed to

`savingby2d`

.

##### Details

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`

.

##### Value

A list containing character vectors giving variable pairings.

##### References

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>.

##### See Also

##### Examples

```
# NOT RUN {
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)
}
# }
```

*Documentation reproduced from package condvis, version 0.5-1, License: GPL (>= 2)*