exploreDAG

Explore whether relationships between fully observed variables in the
specified dataset are consistent with the proposed directed acyclic graph
(DAG) using localTests functionality.

A guidance system for analysis with missing data. It incorporates expert, up-to-date methodology to help researchers choose the most appropriate analysis approach when some data are missing. You provide the available data and the assumed causal structure, including the likely causes of missing data. 'midoc' will advise which analysis approaches can be used, and how best to perform them. 'midoc' follows the framework for the treatment and reporting of missing data in observational studies (TARMOS). Lee et al (2021). <doi:10.1016/j.jclinepi.2021.01.008>.

Elinor Curnow

midoc

A Decision-Making System for Multiple Imputation

Jon Heron

Rosie Cornish

Kate Tilling

James Carpenter

exploreDAG function

<dl><dt>mdag</dt>
<dd>The DAG, specified as a string using dagitty
syntax</dd>
<dt>data</dt>
<dd>A data frame containing all the variables stated in the DAG. All
ordinal variables must be integer-coded and all categorical variables must
be dummy-coded.</dd></dl>

Arguments

Compares data with proposed DAG — exploreDAG

<dl>

<dt>mdag</dt>
<dd>The DAG, specified as a string using dagitty
syntax</dd>


<dt>data</dt>
<dd>A data frame containing all the variables stated in the DAG. All
ordinal variables must be integer-coded and all categorical variables must
be dummy-coded.</dd>

</dl>

exploreDAG: Compares data with proposed DAG

Description

Usage

Value

Arguments

Examples