Description Usage Arguments Details Value Author(s) See Also Examples
This function is used to grow an existing classification tree, typically using more relaxed parameter settings than those used when the tree was created, or if finescale control over the treelearning operation is required.
1 2 3 4 
tree 
an object of class 
clades 
a vector of character strings giving the binary indices matching the labels of the nodes that are to be expanded. Defaults to "0", meaning all subclades are expanded. See below for further details on clade indexing. 
refine 
character string giving the iterative model refinement
method to be used in the partitioning process. Valid options are

iterations 
integer giving the maximum number of trainingclassification
iterations to be used in the splitting process.
Note that this is not necessarily the same as the number of Viterbi training
or Baum Welch iterations to be used in model training, which can be set
using the argument 
nstart 
integer. The number of random starting sets to be chosen for initial kmeans assignment of sequences to groups. Defaults to 20. 
minK 
integer. The minimum number of furications allowed at each inner node of the tree. Defaults to 2 (all inner nodes are bifuricating). 
maxK 
integer. The maximum number of furications allowed at each inner node of the tree. Defaults to 2 (all inner nodes are bifuricating). 
minscore 
numeric between 0 and 1. The minimum acceptable value
for the nth percentile of Akaike weights (where n is
the value given in 
probs 
numeric between 0 and 1. The percentile of Akaike weights
to test against the minimum score threshold given in 
retry 
logical indicating whether failure to split a node based on the criteria outlined in 'minscore' and 'probs' should prompt a second attempt with different initial groupings. These groupings are based on maximum kmer frequencies rather than kmeans division, which can give suboptimal groupings when the cluster sizes are different (due to the upweighting of larger clusters in the kmeans algorithm). 
resize 
logical indicating whether the models should be free to
change size during the training process or if the number of modules
should be fixed. Defaults to TRUE. Only applicable if

maxsize 
integer giving the upper bound on the number of modules in the PHMMs. If NULL, no maximum size is enforced. 
recursive 
logical indicating whether the splitting process
should continue recursively until the discrimination criteria
are not met (TRUE; default), or whether a single split should
take place at each of the nodes specified in 
cores 
integer giving the number of CPUs to use
when training the models (only applicable if

quiet 
logical indicating whether feedback should be printed to the console. 
verbose 
logical indicating whether extra feedback should be printed to the console, including progress at each split. 
... 
further arguments to be passed on to 
The clade indexing system used here is based on character strings, where "0" refers to the root node, "01" is the first child node, "02" is the second child node, "011" is the first child node of the first child node, etc. Note that this means each node cannot have more than 9 child nodes.
an object of class "insect"
.
Shaun Wilkinson
1 2 3 4 5 6 7 8  data(whales)
data(whale_taxonomy)
## split the first node
set.seed(123)
tree < learn(whales, db = whale_taxonomy, recursive = FALSE)
## expand only the first clade
tree < expand(tree, clades = "1")

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