train and method specific methods## S3 method for class 'train':
varImp(object, useModel = TRUE, nonpara = TRUE, scale = TRUE, ...)## S3 method for class 'earth':
varImp(object, value = "gcv", ...)
## S3 method for class 'fda':
varImp(object, value = "gcv", ...)
## S3 method for class 'rpart':
varImp(object, surrogates = FALSE, competes = TRUE, ...)
## S3 method for class 'randomForest':
varImp(object, ...)
## S3 method for class 'gbm':
varImp(object, numTrees, ...)
## S3 method for class 'classbagg':
varImp(object, ...)
## S3 method for class 'regbagg':
varImp(object, ...)
## S3 method for class 'pamrtrained':
varImp(object, threshold, data, ...)
## S3 method for class 'lm':
varImp(object, ...)
## S3 method for class 'mvr':
varImp(object, estimate = NULL, ...)
## S3 method for class 'bagEarth':
varImp(object, ...)
## S3 method for class 'RandomForest':
varImp(object, ...)
## S3 method for class 'rfe':
varImp(object, drop = FALSE, ...)
## S3 method for class 'dsa':
varImp(object, cuts = NULL, ...)
## S3 method for class 'multinom':
varImp(object, ...)
## S3 method for class 'gam':
varImp(object, ...)
## S3 method for class 'cubist':
varImp(object, weights = c(0.5, 0.5), ...)
useModel = FALSE and
only passed to filterVarImp).varImp methodspamr models only)pamr models only)gcv, nsubsets, or rssmvrValpartDSA only)c("varImp.train", "data.frame") for
varImp.train or a matrix for other models.varImp methods, see
filerVarImp.Otherwise:
Linear Models: the absolute value of the t--statistic for each model parameter is used.
Random Forest: varImp.randomForest and
varImp.RandomForest are wrappers around the importance functions from the
maxcompete
argument in rpart.control. This method does not currently provide
class--specific measures of importance when the response is a factor.
Bagged Trees: The same methodology as a single tree is applied to
all bootstrapped trees and the total importance is returned
Boosted Trees: varImp.gbm is a wrapper around the function from that package (see the varImp function tracks the changes in
model statistics, such as the GCV, for each predictor and
accumulates the reduction in the statistic when each
predictor's feature is added to the model. This total reduction
is used as the variable importance measure. If a predictor was
never used in any of the MARS basis functions in the final model
(after pruning), it has an importance
value of zero. Prior to June 2008, the package used an internal function
for these calculations. Currently, the varImp is a wrapper to
the evimp function in the earth package. There are three statistics that can be used to
estimate variable importance in MARS models. Using
varImp(object, value = "gcv") tracks the reduction in the
generalized cross-validation statistic as terms are added.
However, there are some cases when terms are retained
in the model that result in an increase in GCV. Negative variable
importance values for MARS are set to zero.
Alternatively, using
varImp(object, value = "rss") monitors the change in the
residual sums of squares (RSS) as terms are added, which will
never be negative.
Also, the option varImp(object, value ="nsubsets"), which
counts the number of subsets where the variable is used (in the final,
pruned model).
Nearest shrunken centroids: The difference between the class centroids and the overall centroid is used to measure the variable influence (see pamr.predict). The larger the difference between the class centroid and the overall center of the data, the larger the separation between the classes. The training set predictions must be supplied when an object of class pamrtrained is given to varImp.
Cubist: The Cubist output contains variable usage statistics. It gives the percentage of times where each variable was used in a condition and/or a linear model. Note that this output will probably be inconsistent with the rules shown in the output from summary.cubist. At each split of the tree, Cubist saves a linear model (after feature selection) that is allowed to have terms for each variable used in the current split or any split above it. Quinlan (1992) discusses a smoothing algorithm where each model prediction is a linear combination of the parent and child model along the tree. As such, the final prediction is a function of all the linear models from the initial node to the terminal node. The percentages shown in the Cubist output reflects all the models involved in prediction (as opposed to the terminal models shown in the output). The variable importance used here is a linear combination of the usage in the rule conditions and the model.