R/my.rpartco.R

In rpart.plot: Plot 'rpart' Models: An Enhanced Version of 'plot.rpart'

# my.rpartco.R: Copied from rpart package version 3.1.54 and tweaked so
#               args are passed explicitly instead of through "parms".

#SCCS @(#)rpartco.s     1.7 02/07/00
# Compute the x-y coordinates for a tree
my.rpartco <- function(tree, uniform, nspace, minbranch)
{
    frame <- tree$frame
    node <- as.numeric(row.names(frame))
    depth <- tree.depth(node)
    is.leaf <- (frame$var == '<leaf>')
    if(uniform)
        y <- (1 + max(depth) -depth) / max(depth,4)
    else {                    #make y- (parent y) = change in deviance
        y <- dev <- frame$dev
        temp <- split(seq(node), depth)     #depth 0 nodes, then 1, then ...
        parent <- match(floor(node/2), node)
        sibling <- match(ifelse(node %% 2, node - 1, node + 1), node)

        # assign the depths
        for(i in temp[-1L]) {
            temp2 <- dev[parent[i]] - (dev[i] + dev[sibling[i]])
            y[i] <- y[parent[i]] - temp2
        }
        #
        # For some problems, classification & loss matrices in particular
        #   the gain from a split may be 0.  This is ugly on the plot.
        # Hence the "fudge" factor of  .3* the average step
        #
        fudge <-  minbranch * diff(range(y)) / max(depth)
        for(i in temp[-1L]) {
            temp2 <- dev[parent[i]] - (dev[i] + dev[sibling[i]])
            haskids <- !(is.leaf[i] & is.leaf[sibling[i]])
            y[i] <- y[parent[i]] - ifelse(temp2<=fudge & haskids, fudge, temp2)
        }
        y <- y / (max(y))
    }

    # Now compute the x coordinates, by spacing out the leaves and then
    #   filling in
    x   <-  double(length(node))         #allocate, then fill it in below
    x[is.leaf] <- seq(sum(is.leaf))      # leaves at 1, 2, 3, ....
    left.child <- match(node * 2, node)
    right.child <- match(node * 2 + 1, node)

    # temp is a list of non-is.leaf, by depth
    temp <- split(seq(node)[!is.leaf], depth[!is.leaf])
    for(i in rev(temp))
        x[i] <- 0.5 * (x[left.child[i]] + x[right.child[i]])

    if(nspace < 0) return(list(x=x, y=y))

    #
    # Now we get fancy, and try to do overlapping
    #
    #  The basic algorithm is, at each node:
    #      1: get the left & right edges, by depth, for the left and
    #           right sons, of the x-coordinate spacing.
    #      2: find the minimal free spacing.  If this is >0, slide the
    #           right hand son over to the left
    #      3: report the left & right extents of the new tree up to the
    #           parent
    #   A way to visualize steps 1 and 2 is to imagine, for a given node,
    #      that the left son, with all its descendants, is drawn on a
    #      slab of wood.  The left & right edges, per level, give the
    #      width of this board.  (The board is not a rectangle, it has
    #      'stair step' edges). Do the same for the right son.  Now
    #      insert some spacers, one per level, and slide right hand
    #      board over until they touch.  Glue the boards and spacer
    #      together at that point.
    #
    #  If a node has children, its 'space' is considered to extend left
    #    and right by the amount "nspace", which accounts for space
    #    used by the arcs from this node to its children.  For
    #    horseshoe connections nspace usually is 1.
    #
    compress <- function(x, me, depth)
    {
        lson <- me + 1L
        if(is.leaf[lson]) left <- list(left=x[lson], right=x[lson],
                                       depth=depth+1L, sons=lson)
        else {
            left <- compress(x, me+1L, depth+1L)
            x <- left$x
        }

        rson <- me + 1L + length(left$sons)
        if(is.leaf[rson]) right <- list(left=x[rson], right=x[rson],
                                        depth=depth+1L, sons=rson)
        else {
            right <- compress(x, rson, depth+1L)
            x <- right$x
        }

        maxd <- max(left$depth, right$depth) - depth
        mind <- min(left$depth, right$depth) - depth

        # Find the smallest distance between the two subtrees
        #   But only over depths that they have in common
        # 1 is a minimum distance allowed
        slide <- min(right$left[1L:mind] - left$right[1L:mind]) - 1L
        if(slide > 0) { # slide the right hand node to the left
            x[right$sons] <- x[right$sons] - slide;
            x[me] <- (x[right$sons[1L]] + x[left$sons[1L]])/2
        } else slide <- 0

        # report back
        if(left$depth > right$depth) {
            templ <- left$left
            tempr <- left$right
            tempr[1L:mind] <- pmax(tempr[1L:mind], right$right - slide)
        } else {
            templ <- right$left  - slide
            tempr <- right$right - slide
            templ[1L:mind] <- pmin(templ[1L:mind], left$left)
        }

        list(x = x,
             left = c(x[me]- nspace*(x[me] -x[lson]), templ),
             right = c(x[me]- nspace*(x[me] -x[rson]), tempr),
             depth = maxd+ depth, sons=c(me, left$sons, right$sons))
    }
    x <- compress(x, 1L, 1L)$x
    list(x = x, y = y)
}

tree.depth <- function (nodes)
{
    depth <- floor(log(nodes, base = 2) + 1e-7)
    depth - min(depth) # doesn't seem to need as.vector.
}