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treeClust produces a vector of dissimilarities, but these objects are large. This function produces a data frame of data whose inter-point distances are related to the treeClust ones, for use in, for example, k-means.
● Data Source:
CranContrib
● Keywords:
● Alias: tcnewdata
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Print some details about a treeClust object, and the "tbl" element.
● Data Source:
CranContrib
● Keywords:
● Alias: print.treeClust
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This function uses a set of classification or regression trees to build an inter-point dissimilarity in which two points are similar when they tend to fall in the same leaves of trees. The user can pass in a clustering algorithm and/or ask for the dissimilarities or the set of trees.
● Data Source:
CranContrib
● Keywords:
● Alias: treeClust
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Print some details about a treeClust object.
● Data Source:
CranContrib
● Keywords:
● Alias: summary.treeClust
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An rpart regression tree carries the deviance around (in the frame$dev element). This function computes the deviance for classification trees.
● Data Source:
CranContrib
● Keywords:
● Alias: rp.deviance
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Given a treeClust object, or the necessary components, compute all pairwise dissimilarities for input to a clustering algorithm
● Data Source:
CranContrib
● Keywords:
● Alias: tcdist
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This function computes the value of Cramer's V for a two-way table.
● Data Source:
CranContrib
● Keywords:
● Alias: cramer
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It is helpful to know the parent nodes for each tree node. This function creates a matrix with that information.
● Data Source:
CranContrib
● Keywords:
● Alias: make.leaf.paths
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Compute the set of pairwise dissimilarities across all observations in a tree. Each dissimilarity measures the extent to which observations are "far apart" in the tree: the dissimilarity is 0 if the pair land in the same leaf, 1 if they land on leaves that have only the root as common ancestors, and otherwise something intermediate.
● Data Source:
CranContrib
● Keywords:
● Alias: d3.dist
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The "where" element of an rpart object gives the leaf into which each observation used building the tree falls. This produces the equivalent for new data.
● Data Source:
CranContrib
● Keywords:
● Alias: rpart.predict.leaves
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providing 
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