ex_lininter

A dataset containing simulated data where the outcome depends on both main
effects and interaction of two partially observed covariates.

datasets

Implements multiple imputation of missing covariates by
Substantive Model Compatible Fully Conditional Specification.
This is a modification of the popular FCS/chained equations
multiple imputation approach, and allows imputation of missing
covariate values from models which are compatible with the user
specified substantive model.

Jonathan Bartlett

smcfcs

Multiple Imputation of Covariates by Substantive Model
Compatible Fully Conditional Specification

Ruth Keogh

Edouard F. Bonneville

Claus Thorn Ekstrøm

ex_lininter function

A data frame with 1000 rows and 4 variables:<dl>
 <dt>y</dt>
<dd>Continuous outcome</dd> <dt>x1</dt>
<dd>Partially observed normally distributed covariate</dd> <dt>x2</dt>
<dd>Partially observed binary covariate</dd>
</dl>

Format

Simulated example data with continuous outcome and interaction between two partially observed covariates — ex_lininter

A data frame with 1000 rows and 4 variables:<dl>
 <dt>y</dt>
<dd>Continuous outcome</dd>

 <dt>x1</dt>
<dd>Partially observed normally distributed covariate</dd>

 <dt>x2</dt>
<dd>Partially observed binary covariate</dd>


</dl>

ex_lininter: Simulated example data with continuous outcome and interaction between two partially observed covariates

Description

Usage

Arguments

Format