summary.bmem

summary

Calculate bootstrap confidence intervals

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

summary.bmem function

<dl> <dt>object</dt>
<dd>An output object from the function <code>bmem</code></dd> <dt>ci</dt>
<dd><code>norm</code>: normal approximation CI, <code>perc</code>: percentile CI, <code>bc</code>: bias-corrected CI, <code>bca</code>: BCa</dd> <dt>cl</dt>
<dd>Confidence level. Can be a vector.</dd> <dt>...</dt>
<dd>other options can be used for the generic summary function.</dd></dl>

Arguments

Calculate bootstrap confidence intervals — summary.bmem

<dl>

 <dt>object</dt>
<dd>An output object from the function <code>bmem</code></dd>

 <dt>ci</dt>
<dd><code>norm</code>: normal approximation CI, <code>perc</code>: percentile CI, <code>bc</code>: bias-corrected CI, <code>bca</code>: BCa</dd>

 <dt>cl</dt>
<dd>Confidence level. Can be a vector.</dd>

 <dt>...</dt>
<dd>other options can be used for the generic summary function.</dd>

</dl>

summary.bmem: Calculate bootstrap confidence intervals

Description

Usage

Value

Arguments

Details