hdi (version 0.1-9)

plot.clusterGroupBound: Plot output of hierarchical testing of groups of variables

Description

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.

Usage

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

Arguments

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.

Value

Nothing is returned

See Also

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

Examples

# NOT RUN {
<!-- %% the following code is in donttest environment to -->
# }
# NOT RUN {
<!-- %% speed-up computing -->
# }
# NOT RUN {
<!-- %% >>> copy any changes to "../tests/ex-plotClusterGroupBound.R" <<< to ensure -->
# }
# NOT RUN {
<!-- %% code is running -->
# }
# NOT RUN {
## 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))
# }
# NOT RUN {
## 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 = "")
# }