clean_n_pc

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

clean_n_pc function

<dl><dt>n_pc</dt>
<dd>User-supplied n_pc argument. An integer vector.</dd>
<dt>n_cov_vars</dt>
<dd>Number of variables in the covariance matrix</dd></dl>

Arguments

Check and clean n_pc argument — clean_n_pc

<dl>

<dt>n_pc</dt>
<dd>User-supplied n_pc argument. An integer vector.</dd>


<dt>n_cov_vars</dt>
<dd>Number of variables in the covariance matrix</dd>

</dl>

Check and clean n_pc argument

clean_n_pc: Check and clean n_pc argument

Description

Usage

Value

Arguments