README.md

In sbart: Sequential BART for Imputation of Missing Covariates

Description of the package

This program is used for imputing missing covariates by the 'sequential BART' approach. Package provides a function, serBARTfunc, which computes and returns the imputed values.

Installation

devtools::install_github("mjdaniels/SequentialBART")

Main components of the package

The pacakge provides a function, seqBART(), to run the sequential BART model to find the missing covariates. The function takes as arguments 1. X, Covariates having the missing values.

1. Y, Response Variable.

2. Datatype, representing the type of covariate, continuous or binary,

3. Type, representing the type of missingness of the covaraites. It can take 3 values: 0 to represent covariates are MAR with MDM not depending on the response, and 1 or 2 to represent covariates are MAR with MDM depending on the response. If the response is continuous, use type=1 ( linear regression used for imputation), else if it is binary, use type=2 (logistic regression used for imputation).

Rest of the arguments are standard values for proper imputation. Defaults are provided.

Example

sbart::seqBART(xx=Xcovariates, yy=Response, datatype=datatypeValues, type=1)

sbart documentation built on May 1, 2019, 7:23 p.m.