# Plot output of hierarchical testing of groups of variables 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. ```r ## S3 method for class '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`. ## Returns Nothing is returned ## Author(s) Nicolai Meinshausen meinshausen@stat.math.ethz.ch ## 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 ```r ## 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 = "") ```

plotClusterGroupBound.rd function