Conversion between EFA results and CFA structure

Enables a conversion between Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) lavaan-ready structure.

Usage

convert_efa_to_cfa(model, ...)

# S3 method for fa
convert_efa_to_cfa(model, threshold = "max", names = NULL, ...)

efa_to_cfa(model, ...)

Arguments

model

An EFA model (e.g., a psych::fa object).

...

Arguments passed to or from other methods.

threshold

A value between 0 and 1 indicates which (absolute) values from the loadings should be removed. An integer higher than 1 indicates the n strongest loadings to retain. Can also be "max", in which case it will only display the maximum loading per variable (the most simple structure).

names

Vector containing dimension names.

Value

Converted index.

Examples

# \donttest{
library(parameters)
if (require("psych") && require("lavaan")) {
  efa <- psych::fa(attitude, nfactors = 3)

  model1 <- efa_to_cfa(efa)
  model2 <- efa_to_cfa(efa, threshold = 0.3)

  suppressWarnings(anova(
    lavaan::cfa(model1, data = attitude),
    lavaan::cfa(model2, data = attitude)
  ))
}
#> Loading required package: psych
#> 
#> Attaching package: ‘psych’
#> The following object is masked from ‘package:mclust’:
#> 
#>     sim
#> The following objects are masked from ‘package:ggplot2’:
#> 
#>     %+%, alpha
#> The following object is masked from ‘package:boot’:
#> 
#>     logit
#> Loading required package: lavaan
#> This is lavaan 0.6-15
#> lavaan is FREE software! Please report any bugs.
#> 
#> Attaching package: ‘lavaan’
#> The following object is masked from ‘package:psych’:
#> 
#>     cor2cov
#> Loading required namespace: GPArotation
#> 
#> Chi-Squared Difference Test
#> 
#>                                      Df    AIC    BIC   Chisq Chisq diff  RMSEA
#> lavaan::cfa(model2, data = attitude) 10 1540.5 1565.7  9.1827                  
#> lavaan::cfa(model1, data = attitude) 12 1549.8 1572.2 22.4374     13.255 0.4331
#>                                      Df diff Pr(>Chisq)   
#> lavaan::cfa(model2, data = attitude)                      
#> lavaan::cfa(model1, data = attitude)       2   0.001324 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# }