Description Details Author(s) References See Also Examples
Fits a multivariate model of decision trees for multiple, continuous outcome variables. A model for each outcome variable is fit separately, selecting predictors that explain covariance in the outcomes. Built on top of 'gbm', which fits an ensemble of decision trees to univariate outcomes.
The most important function is mvtb
, which fits the multivariate tree boosting model. See ?mvtb
for details. The fitted model objects have summary
, print
, plot
and predict
methods. Additionally, mvtb.ri
to computes the relative influence of each predictor, and mvtb.covex
computes an estimate of the covariance explained in pairs of outcomes by predictors. These tables can be displayed as heatmaps using mvtb.heat
. Examples for fitting, tuning and interpreting the models are available in the help pages and package vignettes:
vignette("mvtboost_wellbeing")
Patrick Miller [aut, cre], Daniel B. McArtor [aut]
Maintainer: Patrick Miller <patrick.mil10@gmail.com>
Miller P.J., Lubke G.H, McArtor D.B., Bergeman C.S. (2015) Finding structure in data: A data mining alternative to multivariate multiple regression. Psychological Methods.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | data(wellbeing)
Y <- wellbeing[,21:26]
X <- wellbeing[,1:20]
Ys <- scale(Y)
cont.id <- unlist(lapply(X,is.numeric))
Xs <- scale(X[,cont.id])
res <- mvtb(Y=Ys,X=Xs)
summary(res)
plot(res,predictor.no = 8)
predict(res,newdata=Xs)
covex <- mvtb.covex(res, Y=Ys, X=Xs)
mvtb.cluster(covex)
par(mar=c(4,7,1,1))
mvtb.heat(covex,cexRow=.8)
par(mar=c(5,5,1,1))
mvtb.heat(t(mvtb.ri(res)),cexRow=.8,cexCol=1,dec=0)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.