apply_locf_nocb

Imputes missing values in a matrix by applying Last Observation Carried Forward (LOCF) followed by
Next Observation Carried Backward (NOCB) for each subject.

Implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.

Jungang Zou

SBMTrees

Sequential Imputation with Bayesian Trees Mixed-Effects Models
for Longitudinal Data

Liangyuan Hu

Robert McCulloch

Rodney Sparapani

Charles Spanbauer

apply_locf_nocb function

<dl><dt>X</dt>
<dd>A matrix where rows represent observations and columns represent variables.</dd>
<dt>subject_id</dt>
<dd>A vector of subject IDs corresponding to the rows of <code>X</code>.</dd></dl>

Arguments

Impute Missing Values Using LOCF and NOCB — apply_locf_nocb

<dl>

<dt>X</dt>
<dd>A matrix where rows represent observations and columns represent variables.</dd>


<dt>subject_id</dt>
<dd>A vector of subject IDs corresponding to the rows of <code>X</code>.</dd>

</dl>

apply_locf_nocb: Impute Missing Values Using LOCF and NOCB

Description

Usage

Value

Arguments

Examples