Calculate the proportion of variables used in tree splits, and average summary stats of tree heights and leaf sizes

Description

Calculates the proportion of particles which use each input to make a tree split and the proportion of all splits in trees of each particle that correspond to each input variable; also provides tree height and leaf size summary information

Usage

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## S3 method for class 'dynaTree'
varpropuse(object)
## S3 method for class 'dynaTree'
varproptotal(object)
## S3 method for class 'dynaTree'
treestats(object)

Arguments

object

a "dynaTree"-class object built by dynaTree

Details

varpropuse gives the proportion of times a particle uses each input variable in a tree split; varproptotal gives the proportion of total uses by the tree in each particle (i.e., averaged over the total number of splits used in the tree).

Usually, varpropuse returns a vector of (nearly) all ones unless there are variables which are not useful in predicting the response. Using model = "linear" is not recommended for this sort of variable selection.

treestats returns the average tree height, and the average leaf size, both active and retired

Value

For varprop*, a vector of proportions of length ncol(object$X)) is returned; for treestats a 1-row, 4-column data.frame is returned

Author(s)

Robert B. Gramacy rbgramacy@chicagobooth.edu,
Matt Taddy taddy@chicagobooth.edu, and
Christoforos Anagnostopoulos christoforos.anagnostopoulos06@imperial.ac.uk

References

Gramacy, R.B., Taddy, M.A., and S. Wild (2011). “Variable Selection and Sensitivity Analysis via Dynamic Trees with an Application to Computer Code Performance Tuning” arXiv:1108.4739

http://bobby.gramacy.com/r_packages/dynaTree/

See Also

dynaTree, sens.dynaTree, relevance.dynaTree

Examples

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## ffit a dynaTree model to the Ozone data
X <- airquality[,2:4]
y <- airquality$Ozone
na <- apply(is.na(X), 1, any) | is.na(y)
out <- dynaTree(X=X[!na,], y=y[!na])

## obtain variable usage proportions
varpropuse(out)
varproptotal(out)

## gather relevance statistics which are more meaningful
out <- relevance(out)
boxplot(out$relevance)
abline(h=0, col=2, lty=2)

## obtain tree statistics
treestats(out)

## clean up
deletecloud(out)

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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