miceadds: tw.imputation – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

tw.imputation

Two-Way Imputation

Description

Two-way imputation using the simple method of Sijtsma and van der Ark (2003) and the MCMC based imputation of van Ginkel, van der Ark, Sijtsma and Vermunt (2007).

Usage

tw.imputation(data, integer=FALSE)

tw.mcmc.imputation(data, iter=100, integer=FALSE)

Arguments

`data`	Matrix of item responses corresponding to a scale
`integer`	A logical indicating whether imputed values should be integers. The default is `FALSE`.
`iter`	Number of iterations

Details

For persons p and items i, the two-way imputation is conducted by posing a linear model of tau-equivalent measurements:

X_{pi}=θ_p + b_i + \varepsilon_{ij}

If the score X_{pi} is missing then it is imputed by

\hat{X}_{pi}=\tilde{X}_p + b_i

where \tilde{X}_p is the person mean of person p of the remaining items with observed responses.

The two-way imputation can also be seen as a scaling procedure to obtain a scale score which takes different item means into account.

Value

A matrix with original and imputed values

References

Sijtsma, K., & Van der Ark, L. A. (2003). Investigation and treatment of missing item scores in test and questionnaire data. Multivariate Behavioral Research, 38(4), 505-528. doi: 10.1207/s15327906mbr3804_4

Van Ginkel, J. R., Van der Ark, A., Sijtsma, K., & Vermunt, J. K. (2007). Two-way imputation: A Bayesian method for estimating missing scores in tests and questionnaires, and an accurate approximation. Computational Statistics & Data Analysis, 51(8), 4013-4027. doi: 10.1016/j.csda.2006.12.022

Examples

#############################################################################
# EXAMPLE 1: Two-way imputation data.internet
#############################################################################

data(data.internet)
data <- data.internet

#***
# Model 1: Two-way imputation method of Sijtsma and van der Ark (2003)
set.seed(765)
dat.imp <- miceadds::tw.imputation( data )
dat.imp[ 278:281,]
  ##       IN9     IN10    IN11     IN12
  ##   278   5 4.829006 5.00000 4.941611
  ##   279   5 4.000000 4.78979 4.000000
  ##   280   7 4.000000 7.00000 7.000000
  ##   281   4 3.000000 5.00000 5.000000

## Not run: 
#***
# Model 2: Two-way imputation method using MCMC
dat.imp <- miceadds::tw.mcmc.imputation( data, iter=3)
dat.imp[ 278:281,]
  ##       IN9     IN10     IN11     IN12
  ##   278   5 6.089222 5.000000 3.017244
  ##   279   5 4.000000 5.063547 4.000000
  ##   280   7 4.000000 7.000000 7.000000
  ##   281   4 3.000000 5.000000 5.000000

## End(Not run)

miceadds

Some Additional Multiple Imputation Functions, Especially for 'mice'

v3.11-6

GPL (>= 2)

Authors

Alexander Robitzsch [aut,cre] (<https://orcid.org/0000-0002-8226-3132>), Simon Grund [aut] (<https://orcid.org/0000-0002-1290-8986>), Thorsten Henke [ctb]

Initial release

2021-01-21 11:48:47