ex_linquad

A dataset containing simulated data where the outcome depends quadratically
on a partially observed covariate.

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_linquad function

A data frame with 1000 rows and 5 variables:<dl>
 <dt>y</dt>
<dd>Continuous outcome</dd> <dt>z</dt>
<dd>Fully observed covariate, with linear effect on outcome</dd> <dt>x</dt>
<dd>Partially observed normally distributed covariate, with quadratic effect on outcome</dd> <dt>xsq</dt>
<dd>The square of x, which thus has missing values also</dd> <dt>v</dt>
<dd>An auxiliary variable (i.e. not contained in the substantive model)</dd>
</dl>

Format

Simulated example data with continuous outcome and quadratic covariate effects — ex_linquad

A data frame with 1000 rows and 5 variables:<dl>
 <dt>y</dt>
<dd>Continuous outcome</dd>

 <dt>z</dt>
<dd>Fully observed covariate, with linear effect on outcome</dd>

 <dt>x</dt>
<dd>Partially observed normally distributed covariate, with quadratic effect on outcome</dd>

 <dt>xsq</dt>
<dd>The square of x, which thus has missing values also</dd>

 <dt>v</dt>
<dd>An auxiliary variable (i.e. not contained in the substantive model)</dd>


</dl>

ex_linquad: Simulated example data with continuous outcome and quadratic covariate effects

Description

Usage

Arguments

Format