bmem.mi.boot

Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models. Details about the methods used can be found in these articles. Zhang and Wang (2003) <doi:10.1007/s11336-012-9301-5>. Zhang (2014) <doi:10.3758/s13428-013-0424-0>.

Zhiyong Zhang

bmem

Mediation Analysis with Missing Data Using Bootstrap

bmem.mi.boot function

<dl> <dt>x</dt>
<dd>A data set</dd> <dt>ram</dt>
<dd>RAM path for the mediaiton model</dd> <dt>indirect</dt>
<dd>A vector of indirect effec</dd> <dt>v</dt>
<dd>Indices of variables used in the mediation model. If omitted, all variables are used.</dd> <dt>m</dt>
<dd>Number of imputations</dd> <dt>boot</dt>
<dd>Number of bootstraps. Default is 1000.</dd> <dt>moment</dt>
<dd>Select mean structure or covariance analysis. moment=FALSE, covariance analysis. moment=TRUE, mean and covariance analysis.</dd> <dt>...</dt>
<dd>Other options for <code>sem</code> function can be used.</dd></dl>

Arguments

Bootstrap for multiple imputation — bmem.mi.boot

<dl>

 <dt>x</dt>
<dd>A data set</dd>

 <dt>ram</dt>
<dd>RAM path for the mediaiton model</dd>

 <dt>indirect</dt>
<dd>A vector of indirect effec</dd>

 <dt>v</dt>
<dd>Indices of variables used in the mediation model. If omitted, all variables are used.</dd>

 <dt>m</dt>
<dd>Number of imputations</dd>

 <dt>boot</dt>
<dd>Number of bootstraps. Default is 1000.</dd>

 <dt>moment</dt>
<dd>Select mean structure or covariance analysis. moment=FALSE, covariance analysis. moment=TRUE, mean and covariance analysis.</dd>

 <dt>...</dt>
<dd>Other options for <code>sem</code> function can be used.</dd>

</dl>

bmem.mi.boot: Bootstrap for multiple imputation

Description

Usage

Arguments