varimp.output

0th

Percentile

Variable importance matrix containing the decrease in predictive accuracy after permuting the variables across all trees

Values of variable 'm' in the oob cases are randomly permuted and R2 obtained through variable-m-permuted oob data is subtracted from R2 obtained on untouched oob data. The average of this number over all the trees in the forest is the raw importance score for variable m.

Usage
varimp.output(varimp_matrix)
Arguments
varimp_matrix

a matrix containing decrease in predictive accuracy for all variables for each tree

Value

An object of class '>varimp.output.

References

Strobl, C., Malley, J. and Tutz, G. (2009) An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests, Psychol Methods, 14, 323-348.

Aliases
  • varimp.output
Documentation reproduced from package mobForest, version 1.3.1, License: GPL (>= 2)

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