vimp.boostmtree: Variable Importance

In boostmtree: Boosted Multivariate Trees for Longitudinal Data

Variable Importance

Description

Calculate VIMP score for each of the individual covariates or a joint VIMP of multiple covariates.

Usage

vimp.boostmtree(object,
                x.names = NULL,
                joint = FALSE)

Arguments

object	A boosting object of class (boostmtree, grow) or class (boostmtree, predict).
x.names	Names of the x-variables for which VIMP is requested. If NULL, VIMP is calcuated for all the covariates
joint	Estimate individual VIMP for each covariate from x.names or a joint VIMP for all covariates combine.

Details

Variable Importance (VIMP) is calcuated for each of the covariates individually or a joint VIMP is calulated for all the covariates specfied in x.names.

Author(s)

Hemant Ishwaran, Amol Pande and Udaya B. Kogalur

References

Friedman J.H. Greedy function approximation: a gradient boosting machine, Ann. of Statist., 5:1189-1232, 2001.

Examples

## Not run: 
##------------------------------------------------------------
## Synthetic example (Response is continuous)
## VIMP is based on in-sample CV using out of bag data
##-------------------------------------------------------------
#simulate the data
dta <- simLong(n = 50, N = 5, rho =.80, model = 2,family = "Continuous")$dtaL

#basic boosting call
boost.grow <- boostmtree(dta$features, dta$time, dta$id, dta$y,
              family = "Continuous", M = 300,cv.flag = TRUE)
vimp.grow <- vimp.boostmtree(object = boost.grow,x.names=c("x1","x2"),joint = FALSE)
vimp.joint.grow <- vimp.boostmtree(object = boost.grow,x.names=c("x1","x2"),joint = TRUE)

##------------------------------------------------------------
## Synthetic example (Response is continuous)
## VIMP is based on test data
##-------------------------------------------------------------
#simulate the data
dtaO <- simLong(n = 100, ntest = 100, N = 5, rho =.80, model = 2, family = "Continuous")

## save the data as both a list and data frame
dtaL <- dtaO$dtaL
dta <- dtaO$dta

## get the training data
trn <- dtaO$trn

#basic boosting call
boost.grow <- boostmtree(dtaL$features[trn,], dtaL$time[trn], dtaL$id[trn], dtaL$y[trn],
              family = "Continuous", M = 300)
boost.pred <- predict(boost.grow,dtaL$features[-trn,], dtaL$time[-trn], dtaL$id[-trn],
              dtaL$y[-trn])
vimp.pred <- vimp.boostmtree(object = boost.pred,x.names=c("x1","x2"),joint = FALSE)
vimp.joint.pred <- vimp.boostmtree(object = boost.pred,x.names=c("x1","x2"),joint = TRUE)


## End(Not run)

boostmtree documentation built on March 18, 2022, 6:54 p.m.