mzipmed_data

Data to be used in the mzipmed package examples

datasets

We implement functions allowing for mediation analysis to be performed in cases where the mediator is a count variable with excess zeroes. First a function is provided allowing users to perform analysis for zero-inflated count variables using the marginalized zero-inflated Poisson (MZIP) model (Long et al. 2014 <DOI:10.1002/sim.6293>). Using the counterfactual approach to mediation and MZIP we can obtain natural direct and indirect effects for the overall population. Using delta method processes variance estimation can be performed instantaneously. Alternatively, bootstrap standard errors can be used. We also provide functions for cases with exposure-mediator interactions with four-way decomposition of total effect.

Andrew Sims

mzipmed

Mediation using MZIP Model

Dustin Long

Hemant Tiwari

Leann Long

mzipmed_data function

A dataframe with 500 rows and 10 variables.<dl>
<dt>X</dt>
<dd>Simulated binary exposure ~Bernoulli(0.5)</dd><dt>C1</dt>
<dd>Simulated covariate ~Normal(0,1)</dd><dt>C2</dt>
<dd>Simulated covariate ~Beta(2,2)</dd><dt>ziM</dt>
<dd>Zero-inflated count mediator based on X,C1,C2</dd><dt>lmM</dt>
<dd>Continuous mediator based on X,C1,C2 with error term ~Normal(0,4)</dd><dt>binM</dt>
<dd>Binary mediator based on X,C1,C2</dd><dt>lmY</dt>
<dd>Continuous outcome to be used for ziM</dd><dt>binY</dt>
<dd>Binary outcome to be used for ziM</dd><dt>ziY1</dt>
<dd>Zero-inflated count outcome to be used for lmM</dd><dt>ziY2</dt>
<dd>Zero-inflated count outcome to be used for binM</dd>
</dl>@source Simulated to serve as an example
@examples
data(mzipmed_data)

Format

Data to be used in the mzipmed package examples — mzipmed_data

A dataframe with 500 rows and 10 variables.<dl>
<dt>X</dt>
<dd>Simulated binary exposure ~Bernoulli(0.5)</dd>

<dt>C1</dt>
<dd>Simulated covariate ~Normal(0,1)</dd>

<dt>C2</dt>
<dd>Simulated covariate ~Beta(2,2)</dd>

<dt>ziM</dt>
<dd>Zero-inflated count mediator based on X,C1,C2</dd>

<dt>lmM</dt>
<dd>Continuous mediator based on X,C1,C2 with error term ~Normal(0,4)</dd>

<dt>binM</dt>
<dd>Binary mediator based on X,C1,C2</dd>

<dt>lmY</dt>
<dd>Continuous outcome to be used for ziM</dd>

<dt>binY</dt>
<dd>Binary outcome to be used for ziM</dd>

<dt>ziY1</dt>
<dd>Zero-inflated count outcome to be used for lmM</dd>

<dt>ziY2</dt>
<dd>Zero-inflated count outcome to be used for binM</dd>


</dl>

@source Simulated to serve as an example
@examples
data(mzipmed_data)

mzipmed_data: Data to be used in the mzipmed package examples

Description

Usage

Arguments

Format