Calculate the proportion of variables used in tree splits, and average summary stats of tree heights and leaf sizes
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
## S3 method for class 'dynaTree' varpropuse(object) ## S3 method for class 'dynaTree' varproptotal(object) ## S3 method for class 'dynaTree' treestats(object)
object: a "dynaTree"-class object built by dynaTreevarpropuse 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
For varprop*, a vector of proportions of length ncol(object$X)) is returned; for treestats a 1-row, 4-column data.frame is returned
Gramacy, R.B., Taddy, M.A., and S. Wild (2011). Variable Selection and Sensitivity Analysis via Dynamic Trees with anApplication to Computer Code Performance Tuning
arXiv:1108.4739
https://bobby.gramacy.com/r_packages/dynaTree/
Robert B. Gramacy rbg@vt.edu ,
Matt Taddy and Christoforos Anagnostopoulos
dynaTree, sens.dynaTree, relevance.dynaTree
## ffit a dynaTree model to the Ozone data X <- airquality[,2:4] y <- airquality$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)