mice_impute_all_NA

Sequential imputations in case that some of the columns are
not imputed due to collinearity, etc.

internal

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) <doi:10.3758/s13428-017-1013-4>.

Tobias Busch

mifa

Multiple Imputation for Exploratory Factor Analysis

Vahid Nassiri

Anikó Lovik

Geert  Molenberghs

Geert  Verbeke

mice_impute_all_NA function

<dl><dt>data</dt>
<dd>A data frame with missing values coded as <code>NA</code>.</dd></dl>

Arguments

Sequential Imputation — mice_impute_all_NA

<dl>

<dt>data</dt>
<dd>A data frame with missing values coded as <code>NA</code>.</dd>

</dl>

mice_impute_all_NA: Sequential Imputation

Description

Usage

Value

Arguments

Details