BART: bartModelMatrix – R documentation

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bartModelMatrix

Create a matrix out of a vector or data.frame

Description

The external BART functions operate on matrices in memory. Therefore, if the user submits a vector or data.frame, then this function converts it to a matrix. Also, it determines the number of cutpoints necessary for each column when asked to do so.

Usage

bartModelMatrix(X, numcut=0L, usequants=FALSE, type=7,
                rm.const=FALSE, cont=FALSE, xinfo=NULL)

Arguments

`X`	A vector or data.frame to create the matrix from.
`numcut`	The maximum number of cutpoints to consider. If `numcut=0`, then just return a matrix; otherwise, return a list containing a matrix `X`, a vector `numcut` and a list `xinfo`.
`usequants`	If `usequants` is `FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, then quantiles are used for the cutpoints.
`type`	Determines which quantile algorithm is employed.
`rm.const`	Whether or not to remove constant variables.
`cont`	Whether or not to assume all variables are continuous.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.

Examples

set.seed(99)

a <- rbinom(10, 4, 0.4)

table(a)

x <- runif(10)

df <- data.frame(a=factor(a), x=x)

b <- bartModelMatrix(df)

b

b <- bartModelMatrix(df, numcut=9)

b

b <- bartModelMatrix(df, numcut=9, usequants=TRUE)

b

## Not run: 
    f <- bartModelMatrix(as.character(a))

## End(Not run)

BART

Bayesian Additive Regression Trees

v2.9

GPL (>= 2)

Authors

Robert McCulloch [aut], Rodney Sparapani [aut, cre], Charles Spanbauer [aut], Robert Gramacy [aut], Matthew Pratola [aut], Martyn Plummer [ctb], Nicky Best [ctb], Kate Cowles [ctb], Karen Vines [ctb]

Initial release

2020-12-21