Multiple Imputation for Exploratory Factor Analysis

Impute the covariance matrix of incomplete data so that factor analysis can be performed. Imputations are made using multiple imputation by Multivariate Imputation with Chained Equations (MICE) and combined with Rubin's rules. Parametric Fieller confidence intervals and nonparametric bootstrap confidence intervals can be obtained for the variance explained by different numbers of principal components. The method is described in Nassiri et al. (2018) .


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0.2.0 by Tobias Busch, a year ago

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Authors: Vahid Nassiri [aut] , Anikó Lovik [aut] , Geert Molenberghs [aut] , Geert Verbeke [aut] , Tobias Busch [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports stats, mice, dplyr, checkmate

Suggests psych, testthat, knitr, rmarkdown, ggplot2, tidyr, covr

See at CRAN