mvtboost

Tree boosting for multivariate outcomes in R, built on gbm. Estimates a multivariate additive model of decision trees by iteratively selecting predictors that explain covariance in the outcomes.

This package can be installed directly from CRAN:

install.packages("mvtboost")
library(mvtboost)

The most recent version can be installed directly from github using the devtools package:

devtools::install_github("patr1ckm/mvtboost")

Example usage

data("mpg",package="ggplot2")
Y <- mpg[,c("cty","hwy")]      
X <- mpg[,c("manufacturer", "displacement", "year", 
          "cylinder", "transmission", "drive"", "class")]

out <- mvtb(Y=Y,X=X,           # data
        n.trees=1000,          # number of trees
        shrinkage=.01,         # shrinkage or learning rate
        interaction.depth=3)   # tree or interaction depth
?mvtb
        

Interpret the model

summary(out)                   # best trees, relative influences, and covex
mvtb.ri(out)                   # relative influences

yhat <- predict(out,newdata=X) # predictions

par(mfcol=c(1,2))              # model implied effects of displacement for cty and hwy
plot(out,1,predictor.no=2)
plot(out,2,predictor.no=2)

covex <- mvtb.covex(out)       # compute covariance explained in outcomes by predictors
mvtb.heat(covex)               # heat map of the clustered covariance explained matrix
mvtb.cluster(covex)            # clustered covariance explained 

mvtb.nonlin(out,Y=Y,X=X)       # indicators of predictors with nonlinear effects

Tune the model

out2 <- mvtb(Y=Y,X=X,
        n.trees=1000, 
        shrinkage=.01,
        interaction.depth=3,
        
        bag.fraction=.5,      # fit each tree to a sub sample of this fraction
        train.fraction=.5,    # only fit the model to this fraction of the data set
        cv.folds=3,           # number of cross-validation folds
        mc.cores=3)           # run the cross-validation in parallel (not tested on windows)

Vignettes

vignette("mvtboost_vignette")
vignette("mvtboost_wellbeing")