snpRF (version 0.4)

getTree: Extract a single tree from a forest.

Description

This function extracts the structure of a tree from a snpRF object.

Usage

getTree(rfobj, k=1, labelVar=FALSE)

Arguments

rfobj
a snpRF object.
k
which tree to extract?
labelVar
Should better labels be used for splitting variables and predicted class?

Value

A matrix (or data frame, if labelVar=TRUE) with six columns and number of rows equal to total number of nodes in the tree. The six columns are:
left daughter
the row where the left daughter node is; 0 if the node is terminal
right daughter
the row where the right daughter node is; 0 if the node is terminal
split var
which variable was used to split the node; 0 if the node is terminal
split point
where the best split is; see Details for categorical predictor
status
is the node terminal (-1) or not (1)
prediction
the prediction for the node; 0 if the node is not terminal

Details

For numerical predictors, data with values of the variable less than or equal to the splitting point go to the left daughter node.

For categorical predictors, the splitting point is represented by an integer, whose binary expansion gives the identities of the categories that goes to left or right. For example, if a predictor has four categories, and the split point is 13. The binary expansion of 13 is (1, 0, 1, 1) (because $13 = 1*2^0 + 0*2^1 + 1*2^2 + 1*2^3$), so cases with categories 1, 3, or 4 in this predictor get sent to the left, and the rest to the right.

See Also

snpRF

Examples

Run this code
data(snpRFexample)
## Look at the third trees in the forest.
getTree(snpRF(x.autosome=autosome.snps,x.xchrom=xchrom.snps,
              xchrom.names=xchrom.snps.names,x.covar=covariates,
              y=phenotype,ntree=10), 3, labelVar=TRUE)

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