mi_dstat

Computes Cohen's d effect size indicating whether missingness
on a variable is related to other variables (covariates).

Contains functions for multiple imputation which
complements existing functionality in R.
In particular, several imputation methods for the
mice package (van Buuren & Groothuis-Oudshoorn, 2011,
<doi:10.18637/jss.v045.i03>) are implemented.
Main features of the miceadds package include
plausible value imputation (Mislevy, 1991,
<doi:10.1007/BF02294457>), multilevel imputation for
variables at any level or with any number of hierarchical
and non-hierarchical levels (Grund, Luedtke & Robitzsch,
2018, <doi:10.1177/1094428117703686>; van Buuren, 2018,
Ch.7, <doi:10.1201/9780429492259>), imputation using
partial least squares (PLS) for high dimensional
predictors (Robitzsch, Pham & Yanagida, 2016),
nested multiple imputation (Rubin, 2003,
<doi:10.1111/1467-9574.00217>), substantive model
compatible imputation (Bartlett et al., 2015,
<doi:10.1177/0962280214521348>), and features
for the generation of synthetic datasets
(Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>;
Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).

Alexander Robitzsch

miceadds

Some Additional Multiple Imputation Functions, Especially for
'mice'

mi_dstat function

<dl> <dt>dat</dt>
<dd>Data frame</dd></dl>

Arguments

Cohen's d Effect Size for Missingness Indicators — mi_dstat

<dl>

 <dt>dat</dt>
<dd>Data frame</dd>

</dl>

mi_dstat: Cohen's d Effect Size for Missingness Indicators

Description

Usage

Value

Arguments

Examples