recTree: recursiveTree: cross-validated feature contributions
In forestFloor: Visualizes Random Forests with Feature Contributions

Description Usage Arguments Details Value Author(s) References

internal C++ functions to compute feature contributions for a random Forest

recTree(  vars, obs, ntree, calculate_node_pred, X,Y,majorityTerminal, leftDaughter, 
    rightDaughter, nodestatus, xbestsplit, nodepred, bestvar, 
    inbag, varLevels, OOBtimes, localIncrements)

multiTree(vars, obs, ntree, nClasses,            X,Y,majorityTerminal, leftDaughter,
    rightDaughter, nodestatus, xbestsplit, nodepred, bestvar,
    inbag, varLevels, OOBtimes, localIncrements)

`vars`	number of variables in X
`obs`	number of observations in X
`ntree`	number of trees starting from 1 function should iterate, cannot be higher than columns of inbag
`nClasses`	number of classes in classification forest
`calculate_node_pred`	should the node predictions be recalculated(true) or reused from nodepred-matrix(false & regression)
`X`	X training matrix
`Y`	target vector, factor or regression
`majorityTerminal`	bool, majority vote in terminal nodes? Default is FALSE for regression. Set only to TRUE when binary_reg=TRUE.
`leftDaughter`	a matrix from a the output of randomForest rf$forest$leftDaughter the node.number/row.number of the leftDaughter in a given tree by column
`rightDaughter`	a matrix from a the output of randomForest rf$forest$rightDaughter the node.number/row.number of the rightDaughter in a given tree by column
`nodestatus`	a matrix from a the output of randomForest rf$forest$nodestatus the nodestatus of a given node in a given tree
`xbestsplit`	a matrix from a the output of randomForest rf$forest$xbestsplit. The split point of numeric variables or the binary split of categorical variables. See help file of randomForest::getTree for details of binary expansion for categorical splits.
`nodepred`	a matrix from a the output of randomForest rf$forest$xbestsplit. The inbag target average for regression mode and the majority target class for classification
`bestvar`	a matrix from a the output of randomForest rf$forest$xbestsplit the inbag target average for regression mode and the majority target class for classification
`inbag`	a matrix as the output of randomForest rf$inbag. Contain counts of how many times a sample was selected for a given tree.
`varLevels`	the number of levels of all variables, 1 for continuous or discrete, >1 for categorical variables. This is needed for categorical variables to interpret binary split from xbestsplit.
`OOBtimes`	number of times a certain observation was out-of-bag in the forest. Needed to compute cross-validated feature contributions as these are summed local increments over out-of-bag observations over features divided by this number. In previous implementation(rfFC), articles(see references) feature contributions are summed by all observations and is divived by ntrees.
`localIncrements`	an empty matrix to store localIncrements during computation. As C++ function returns, the input localIncrement matrix contains the feature contributions.

This is function is excuted by the function forestFloor. This is a c++/Rcpp implementation computing feature contributions. The main differences from this implementation and the rfFC-package(Rforge), is that these feature contributions are only summed over out-of-bag samples yields a cross-validation. This implementation allows sample replacement, binary and multi-classification.

no output, the feature contributions are writtten directly to localIncrements input

Soren Havelund Welling

Interpretation of QSAR Models Based on Random Forest Methods, http://dx.doi.org/10.1002/minf.201000173
Interpreting random forest classification models using a feature contribution method, http://arxiv.org/abs/1312.1121

forestFloor documentation built on May 2, 2019, 2:40 a.m.