# Variable Importance Calculate VIMP score for each of the individual covariates or a joint VIMP of multiple covariates. ```r 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 ```r ## 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) ```

vimp.boostmtree function