bmem.cov

Can be used to simulated data for an SEM model.

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.cov function

<dl> <dt>ram</dt>
<dd>An ram model</dd> <dt>obs.variables</dt>
<dd>Names of the observed variables</dd> <dt>moment</dt>
<dd>Whether to use the mean structure</dd> <dt>debug</dt>
<dd>debug mode</dd></dl>

Arguments

Calculate the covariance matrix based on a given ram model — bmem.cov

<dl>

 <dt>ram</dt>
<dd>An ram model</dd>

 <dt>obs.variables</dt>
<dd>Names of the observed variables</dd>

 <dt>moment</dt>
<dd>Whether to use the mean structure</dd>

 <dt>debug</dt>
<dd>debug mode</dd>

</dl>

bmem.cov: Calculate the covariance matrix based on a given ram model

Description

Usage

Arguments