knnTree {knnTree} | R Documentation |
Construct or predict with a knnTree object, which is a set of k-nearest neighbor classifiers, one for each leaf of a tree.
knnTree (trg.set, trg.classes, v = 10, k.vec = seq(1, 31, by = 2), seed = 0, opt.tree = "ignore", opt.tree.size = 4, scaling = 1, prune.function = prune.misclass, one.SE = TRUE, backward = FALSE, max.steps=-1, v.start = 1, leaf.start = 1, verbose = FALSE, debug = 0, fname = "", use.big = FALSE, save.output = "")
trg.set |
data frame or matrix of training data without classifications |
trg.classes |
categorical vector of training set classifications |
v |
numeric, number of blocks for cross-validation |
k.vec |
numeric vector of numbers of k to consider |
seed |
if present, passed to set.seed() to initialize the random
number generator |
opt.tree |
character, giving method by which to choose the size of the tree. Choices are ignore (consider all sizes up to size of first unpruned tree); find (consider all sizes up to size of first pruned tree); fix (use tree size passed in opt.tree.size); max (consider all sizes <= opt.tree.size) |
opt.tree.size |
tree size used if opt.tree = fix or max |
scaling |
numeric describing scaling technique: 0 means do no scaling; 1 means choose between no scaling and scaling each column by its SD; 2 means choose between no scaling and scaling each column by its MAD. |
prune.function |
function to do pruning, normally prune.tree or prune.misclass |
one.SE |
logical; if TRUE, prune and then use one-SE rule |
backward |
logical describing variable selection technique. TRUE means start with all variables and delete them one at a time until there is no improvement; FALSE means start with no variables and add them one at a time. |
max.steps |
numeric giving maximum number of steps to take. If negative, continue until there is no improvement. Default: -1. |
v.start |
number of cross-validation block to start at; for debugging only |
leaf.start |
number of leaf to start at; for debugging only |
verbose |
numeric for debugging purposes. If verbose is 0, no diagnostic output is produced. If verbose > 0, diagnostic output (more as the value increases) is sent to file fname, which is the screen if fname is the empty string. |
debug |
currently not used |
fname |
string naming the file to which diagnostic output is sent if verbose > 0 |
use.big |
logical, TRUE if the C code should try to use a technique that uses more memory but runs faster. |
save.output |
character; if not empty, the resulting object is assigned to results in frame 1 and also dumped to disk in the file named in save.output. This can be useful for parallel processing. |
Object of class knnTree. If the tree has n leaves, this will be a list with n+2 elements. The first is the global tree. The next n elements are the n individual knn.var objects, one per leaf. Each of these objects has two additional pieces: leaf (giving the leaf number) and where (giving the row number of the global tree's frame for this leaf). The n+2-th element of the list is named call and is the call used to create the object.