mvtb.covex

object

vector, matrix, or data.frame for outcome variables with no missing values. To easily compare influences across outcomes and for numerical stability, outcome variables should be scaled to have unit variance.

vector, matrix, or data.frame of predictors. For best performance, continuous predictors should be scaled to have unit variance. Categorical variables should converted to factors.

number of trees to use. Defaults to the minimum number of trees by CV, test, or training error

n.trees

<code>TRUE/FALSE</code>. Return the loss, relative loss, and selected predictors at each iteration as a list

iter.details


Estimate the covariance explained matrix


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.

mvtb.covex: Estimate the covariance explained matrix

Description

Usage

Arguments

Value