Performance of FA / PCA models

Compute indices of model performance for models from the psych package, and for parameters::factor_analysis() and item_omega().

Usage

# S3 method for class 'fa'
model_performance(model, metrics = "all", verbose = TRUE, ...)

Arguments

model

A model object of class fa (e.g., from psych::fa()), principal (e.g., from psych::principal()), or from parameters::factor_analysis() or item_omega().

metrics

Can be "all" or a character vector of metrics to be computed (some of "Chi2", "Chi2_df", "df", "p_Chi2", "RMSA", "RMSA_corrected", "TLI", "RMSEA", and "BIC". For omega-models, can also include "R2" and "Correlation".

verbose

Toggle off warnings.

...

Arguments passed to or from other methods.

Value

A data frame (with one row) and one column per "index" (see metrics).

Details

For omega-models, the columns R2 and Correlation are measures of factor score adequacy. R2 refers to the multiple R square of scores with factors, while Correlation indicates the correlation of scores with factors.

Examples

out <- psych::fa(psychTools::bfi[, 1:25], 5)
model_performance(out)
#> # Indices of model performance
#> 
#> Chi2(185) | p (Chi2) |  RMSA | RMSA_corrected |   TLI | RMSEA |   RMSEA 90% CI |     BIC
#> ----------------------------------------------------------------------------------------
#> 1808.943  |   < .001 | 0.029 |          0.037 | 0.867 | 0.056 | [0.054, 0.058] | 340.528

out <- item_omega(mtcars, n = 3)
model_performance(out)
#> # Indices of model performance
#> 
#> Model             |    Chi2 | df | p (Chi2) |  RMSA | RMSA_corrected |   TLI
#> ----------------------------------------------------------------------------
#> 3-factor solution |  31.796 | 25 |   0.164  | 0.015 |          0.023 |      
#> g-model           | 264.781 | 44 |   < .001 | 0.393 |          0.440 | 0.195
#> 
#> Model             | RMSEA |   RMSEA 90% CI |     BIC |    R2 | Correlation
#> --------------------------------------------------------------------------
#> 3-factor solution | 0.087 | [0.000, 0.181] | -54.848 |       |            
#> g-model           | 0.395 | [0.356, 0.450] | 112.289 | 0.761 |       0.873
#> 
#> Compare the model fit of the 3-factor solution with the g-only model.
#>   If the g-model has smaller RMSA and RMSEA then your items are more
#>   likely to describe a single unidimensional construct. If the 3-factor
#>   model has smaller RMSA and RMSEA then your construct is more likely to
#>   be made up of 3 sub-constructs.