xicuts

This function allows you to create a list that specifies
 the cut-points for the covariates.

Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a complete description of the model at <doi:10.1111/biom.13857>.

Rodney Sparapani

nftbart

Nonparametric Failure Time Bayesian Additive Regression Trees

Robert McCulloch

Matthew Pratola

Hugh Chipman

xicuts function

<dl> <dt>x.train</dt>
<dd>The training matrix to derive cut-points from.</dd> <dt>transposed</dt>
<dd>Whether or not the matrix has been tranposed yet.</dd> <dt>numcut</dt>
<dd>The number of cut-points to create.</dd></dl>

Arguments

Specifying cut-points for the covariates — xicuts

<dl>

 <dt>x.train</dt>
<dd>The training matrix to derive cut-points from.</dd>

 <dt>transposed</dt>
<dd>Whether or not the matrix has been tranposed yet.</dd>

 <dt>numcut</dt>
<dd>The number of cut-points to create.</dd>

</dl>

xicuts: Specifying cut-points for the covariates

Description

Usage

Value

Arguments

Details