Tree structure of projection pursuit classification tree
projbest.node
1-dim optimal projections of each split node
splitCutoff.node
cutoff values of each split node
origclass
original class
origdata
original data
Arguments
form
A character with the name of the class variable.
data
Data frame with the complete data set.
PPmethod
index to use for projection pursuit: 'LDA', 'PDA'
size.p
proportion of variables randomly sampled in each split, default is 1, returns a PPtree.
lambda
penalty parameter in PDA index and is between 0 to 1 . If lambda = 0, no penalty parameter is added and the PDA index is the same as LDA index. If lambda = 1 all variables are treated as uncorrelated. The default value is lambda = 0.1.
...
arguments to be passed to methods
References
Lee, YD, Cook, D., Park JW, and Lee, EK (2013)
PPtree: Projection pursuit classification tree,
Electronic Journal of Statistics, 7:1369-1386.