Standard and conditional variable importance for `cforest', following the permutation principle of the `mean decrease in accuracy' importance in `randomForest'.
# S3 method for constparty
varimp(object, nperm = 1L,
risk = c("loglik", "misclassification"), conditions = NULL,
mincriterion = 0, ...)
# S3 method for cforest
varimp(object, nperm = 1L,
OOB = TRUE, risk = c("loglik", "misclassification"),
conditional = FALSE, threshold = .2, applyfun = NULL,
cores = NULL, ...)A vector of `mean decrease in accuracy' importance scores.
an object as returned by cforest.
the value of the test statistic or 1 - p-value that
must be exceeded in order to include a split in the
computation of the importance. The default mincriterion = 0
guarantees that all splits are included.
a logical determining whether unconditional or conditional computation of the importance is performed.
the value of the test statistic or 1 - p-value 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
conditional = TRUE).
the number of permutations performed.
a logical determining whether the importance is computed from the out-of-bag sample or the learning sample (not suggested).
a character determining the risk to be evaluated.
a list of conditions.
an optional lapply-style function with arguments
function(X, FUN, ...). It is used for computing the variable importances for each tree.
The default is to use the basic lapply
function unless the cores argument is specified (see below).
Extra care is needed to ensure correct seeds are used in
the parallel runs (RNGkind("L'Ecuyer-CMRG") for
example).
numeric. If set to an integer the applyfun is set to
mclapply with the desired number of cores.
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 - p-value
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.
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), 21-34. tools:::Rd_expr_doi("10.1007/s11222-012-9349-1")
Torsten Hothorn, Kurt Hornik, and Achim Zeileis (2006b). Unbiased Recursive Partitioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15(3), 651-674. tools:::Rd_expr_doi("10.1198/106186006X133933")
Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, and Achim Zeileis (2008). Conditional Variable Importance for Random Forests. BMC Bioinformatics, 9, 307. tools:::Rd_expr_doi("10.1186/1471-2105-8-25")
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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