plotClusterGroupBound: Plot output of hierarchical testing of groups of variables
In hdi: High-Dimensional Inference

Description Usage Arguments Value Author(s) See Also Examples

The plot() method for "clusterGroupBound" objects plots the outcome of applying a lower bound on the l1-norm on groups of variables in a hierarchical clustering tree.

1
2
3

## S3 method for class 'clusterGroupBound'
plot(x, cexfactor = 1, yaxis = "members",
     xlab = "", col = NULL, pch = 20, ...)

`x`	an object of `class` `"clusterGroupBound"`, as resulting from `clusterGroupBound()`.
`cexfactor`	numeric expansion factor for the size of the node symbols.
`yaxis`	a string; for the default `"members"`, the hierarchical tree is shown as function of cluster size on the y-axis, whereas the node sizes are proportional to the lower l1-norm of the respective groups of variables. If `yaxis` takes any different value, then this is reversed and the tree is shown against the lower l1-norm on the y-axis, while node sizes are now proportional to the number of elements in each cluster.
`xlab`	label used for the x-axis; by default none.
`col`	the colour of the symbols for the nodes.
`pch`	the plot symbol (see `points`) of the symbols for the nodes.
`...`	optional additional arguments passed to `plot.default`.

Nothing is returned

Nicolai Meinshausen meinshausen@stat.math.ethz.ch

Use clusterGroupBound() to test all groups in a hierarchical clustering tree. Use groupBound() to compute the lower bound for selected groups of variables.

## Create a regression problem with correlated design (n = 10, p = 3):
## a block of size 2 and a block of size 1, within-block correlation is 0.99

set.seed(29)
p   <- 3
n   <- 10

Sigma <- diag(p)
Sigma[1,2] <- Sigma[2,1] <- 0.99

x <- matrix(rnorm(n * p), nrow = n) %*% chol(Sigma)

## Create response with active variable 1
beta    <- rep(0, p)
beta[1] <- 5

y  <- as.numeric(x %*% beta + rnorm(n))

## Compute the lower bound for all groups in a hierarchical clustering tree
cgb5 <- clusterGroupBound(x, y, nsplit = 4) ## use larger value for nsplit!

## Plot the tree with y-axis proportional to the (log) of the number of
## group members and node sizes proportional to the lower l1-norm bound.
plot(cgb5)

## Show the lower bound on the y-axis and node sizes proportional to
## number of group members
plot(cgb5, yaxis = "")