varimp  R Documentation 
Standard and conditional variable importance for ‘cforest’, following the permutation principle of the ‘mean decrease in accuracy’ importance in ‘randomForest’.
## S3 method for class 'constparty' varimp(object, nperm = 1L, risk = c("loglik", "misclassification"), conditions = NULL, mincriterion = 0, ...) ## S3 method for class 'cforest' varimp(object, nperm = 1L, OOB = TRUE, risk = c("loglik", "misclassification"), conditional = FALSE, threshold = .2, applyfun = NULL, cores = NULL, ...)
object 
an object as returned by 
mincriterion 
the value of the test statistic or 1  pvalue that
must be exceeded in order to include a split in the
computation of the importance. The default 
conditional 
a logical determining whether unconditional or conditional computation of the importance is performed. 
threshold 
the value of the test statistic or 1  pvalue of the association
between the variable of interest and a covariate that must be
exceeded inorder to include the covariate in the conditioning
scheme for the variable of interest (only relevant if

nperm 
the number of permutations performed. 
OOB 
a logical determining whether the importance is computed from the outofbag sample or the learning sample (not suggested). 
risk 
a character determining the risk to be evaluated. 
conditions 
a list of conditions. 
applyfun 
an optional 
cores 
numeric. If set to an integer the 
... 
additional arguments, not used. 
NEEDS UPDATE
Function varimp
can be used to compute variable importance measures
similar to those computed by importance
. Besides the
standard version, a conditional version is available, that adjusts for correlations between
predictor variables.
If conditional = TRUE
, the importance of each variable is computed by permuting
within a grid defined by the covariates that are associated (with 1  pvalue
greater than threshold
) to the variable of interest.
The resulting variable importance score is conditional in the sense of beta coefficients in
regression models, but represents the effect of a variable in both main effects and interactions.
See Strobl et al. (2008) for details.
Note, however, that all random forest results are subject to random variation. Thus, before
interpreting the importance ranking, check whether the same ranking is achieved with a
different random seed – or otherwise increase the number of trees ntree
in
ctree_control
.
Note that in the presence of missings in the predictor variables the procedure described in Hapfelmeier et al. (2012) is performed.
A vector of ‘mean decrease in accuracy’ importance scores.
Leo Breiman (2001). Random Forests. Machine Learning, 45(1), 5–32.
Alexander Hapfelmeier, Torsten Hothorn, Kurt Ulm, and Carolin Strobl (2014). A New Variable Importance Measure for Random Forests with Missing Data. Statistics and Computing, 24(1), 2134. doi: 10.1007/s1122201293491
Torsten Hothorn, Kurt Hornik, and Achim Zeileis (2006b). Unbiased Recursive Partitioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15(3), 651674. doi: 10.1198/106186006X133933
Carolin Strobl, AnneLaure Boulesteix, Thomas Kneib, Thomas Augustin, and Achim Zeileis (2008). Conditional Variable Importance for Random Forests. BMC Bioinformatics, 9, 307. doi: 10.1186/14712105825
set.seed(290875) data("readingSkills", package = "party") readingSkills.cf < cforest(score ~ ., data = readingSkills, mtry = 2, ntree = 50) # standard importance varimp(readingSkills.cf) # conditional importance, may take a while... varimp(readingSkills.cf, conditional = TRUE)
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