data.largescale

Large-scale dataset with many cases and few variables
included for testing purposes.

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.largescale function

A data frame with 14000 observations on the following 13 variables.
 The format is<code>'data.frame': 14000 obs. of 13 variables:</code> 
<code> $ id: num 1e+07 1e+07 1e+07 1e+07 1e+07 ...</code> 
<code> $ D1: num 0 0 0 0 1 0 0 0 0 0 ...</code> 
<code> $ D2: num 0 0 0 1 0 1 0 1 0 0 ...</code> 
<code> $ D3: num 0 0 0 0 0 0 0 0 0 0 ...</code> 
<code> $ D4: num 0 0 0 1 0 0 0 1 0 0 ...</code> 
<code> $ D5: num 0 0 0 0 0 1 0 0 0 0 ...</code> 
<code> $ v1: num 118 117 94 106 86 117 96 96 82 95 ...</code> 
<code> $ v2: num 101 101 86 101 65 94 72 75 70 99 ...</code> 
<code> $ v3: num 0 0 0 0 0 1 0 0 0 0 ...</code> 
<code> $ v4: num 3 NA 3 5 2 5 5 5 4 2 ...</code> 
<code> $ v5: num 0 NA 0 0 0 1 0 0 0 0 ...</code> 
<code> $ v6: num 3 3 3 4 NA 1 3 3 2 3 ...</code> 
<code> $ v7: num 51 36 14 47 22 17 13 37 47 38 ...</code>

data.largescale: Large-scale Dataset for Testing Purposes (Many Cases, Few Variables)

Description

Usage

Arguments

Format