data.smallscale

Small-scale dataset for testing purposes (moderate number of cases,
many variables)

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 included.
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>) and substantive model
compatible imputation (Bartlett et al., 2015,
<doi:10.1177/0962280214521348>).

Alexander Robitzsch

miceadds

Some Additional Multiple Imputation Functions, Especially for
'mice'

data.smallscale function

A data frame with 675 observations on the following 164 variables.
 The format is<code>'data.frame': 675 obs. of 164 variables:</code> 
<code> $ v1 : num 3 3 2 3 3 0 1 0 3 NA ...</code> 
<code> $ v2 : num 3 0 1 3 0 0 0 3 2 NA ...</code> 
<code> $ v3 : num 0 0 2 3 2 0 1 0 0 NA ...</code> 
<code> $ v4 : num 1 3 3 3 NA 0 0 0 3 NA ...</code> 
<code> $ v5 : num 0 0 3 3 0 0 3 1 3 3 ...</code> 
<code> $ v6 : num 8 8 9 8 9 9 9 8 9 9 ...</code> 
<code>[...]</code>

data.smallscale: Small-Scale Dataset for Testing Purposes (Moderate Number of Cases, Many Variables)

Description

Usage

Arguments

Format