# 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

1 2 3 4 5 6 | ```
## S3 method for class 'dynaTree'
varpropuse(object)
## S3 method for class 'dynaTree'
varproptotal(object)
## S3 method for class 'dynaTree'
treestats(object)
``` |

### Arguments

`object` |
a |

### 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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## 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)
``` |