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
#> ---------------------------------------------------------------------------------------
#> 3043.118  |   < .001 | 0.029 |          0.037 | 0.674 | 0.074 | [0.072, 0.077] | 1574.7

out <- item_omega(mtcars, n = 3)
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
model_performance(out)
#> # Indices of model performance
#> 
#> Model             |    Chi2 | df | p (Chi2) |  RMSA | RMSA_corrected | RMSEA
#> ----------------------------------------------------------------------------
#> 3-factor solution |  69.778 | 25 |   < .001 | 0.015 |          0.023 | 0.234
#> g-model           | 198.659 | 44 |   < .001 | 0.393 |          0.440 | 0.330
#> 
#> Model             |   RMSEA 90% CI |   BIC |    R2 | Correlation
#> ----------------------------------------------------------------
#> 3-factor solution | [0.175, 0.308] | -16.9 |       |            
#> g-model           | [0.290, 0.385] |  46.2 | 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.