varimp.output: Variable importance matrix containing the decrease in...
In RTIInternational/mobForest: Model Based Random Forest Analysis
Description
Usage
Arguments
Value
References
View source: R/varimp.output.R
Description
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
1
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.
RTIInternational/mobForest documentation built on Aug. 3, 2019, 8:28 a.m.
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Description Usage Arguments Value References
View source: R/varimp.output.R
Description
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
1 | 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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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