Print Results from AI-Assisted EFA Refinement — print_efa

Prints a structured summary of the key outputs from optimal_efa_with_ai, including the removed items, final RMSEA, iteration count, final factor structure, step-by-step log, and final fit indices.

Usage

print_efa_results(res)

Arguments

res: A list returned by optimal_efa_with_ai, containing at least the components removed_items, final_rmsea, iterations, final_structure, steps_log, and bondades_original.

Details

The function prints to the console in the following order:

A “Summary” block with:
- removed_items: the items removed during refinement.
- final_rmsea: the scaled RMSEA after the last iteration.
- iterations: total number of iterations performed.
“Factor Structure” showing the final loadings/data frame.
“Step Log” displaying the data frame of each removal step.
“Final Fit Indices” printing the bondades_original object as returned by the package's internal EFA engine.

Value

invisibly returns NULL. Used for its printing side effect.

Author

Dr. José Ventura-León

Examples

# \donttest{
data(Data_Expectativas)
res_efa <- optimal_efa_with_ai(Data_Expectativas, items = paste0("EAF", 1:10),
                               name_items = "EAF", n_factors = 2,
                               verbose = FALSE)
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -1.201524e-16) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -2.509295e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -8.897683e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -3.423595e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -6.845765e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -1.808871e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -1.891747e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= 5.555843e-17) 
#>    is close to zero. This may be a symptom that the model is not identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -6.055497e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -4.916609e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -3.276730e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= 3.344685e-17) 
#>    is close to zero. This may be a symptom that the model is not identified.
#> Warning: lavaan->lav_object_post_check():  
#>    some estimated ov variances are negative
#> Warning: lavaan->lav_object_post_check():  
#>    some estimated ov variances are negative
#> Warning: lavaan->lav_object_post_check():  
#>    some estimated ov variances are negative
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -7.082614e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= 9.413882e-18) 
#>    is close to zero. This may be a symptom that the model is not identified.
#> Warning: lavaan->lav_object_post_check():  
#>    some estimated ov variances are negative
#> Warning: lavaan->lav_object_post_check():  
#>    some estimated ov variances are negative
#> Warning: lavaan->lav_object_post_check():  
#>    some estimated ov variances are negative
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -1.889844e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= 3.444830e-17) 
#>    is close to zero. This may be a symptom that the model is not identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -6.271269e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -2.822070e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -4.266688e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -1.425497e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -6.877305e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -3.112619e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -8.897683e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -3.423595e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= -2.039337e-17) 
#>    is smaller than zero. This may be a symptom that the model is not 
#>    identified.
#> Warning: lavaan->lav_model_vcov():  
#>    The variance-covariance matrix of the estimated parameters (vcov) does not 
#>    appear to be positive definite! The smallest eigenvalue (= 1.350965e-17) 
#>    is close to zero. This may be a symptom that the model is not identified.
print_efa_results(res_efa)
#> 
#> ===== Resumen =====
#> Ítems eliminados: EAF1, EAF7 
#> RMSEA final: 0.069
#> Iteraciones:   2
#> ===== Estructura factorial (EFA UD) =====
#>   Items        f1        f2
#> 1  EAF6 0.8488690 0.0000000
#> 2  EAF3 0.7337100 0.0000000
#> 3  EAF5 0.6924949 0.0000000
#> 4  EAF9 0.0000000 0.9562016
#> 5  EAF8 0.0000000 0.9091470
#> 6 EAF10 0.0000000 0.8093821
#> 7  EAF4 0.0000000 0.6706327
#> 8  EAF2 0.0000000 0.6649135
#> 
#> ===== Log de pasos =====
#>   step removed_item        reason rmsea
#> 1    1         EAF1  Mejora RMSEA 0.064
#> 2    2         EAF7 Cross-loading 0.064
#> 
#> ===== Bondades de ajuste finales =====
#>   Factores chisq.scaled df.scaled  srmr  wrmr cfi.scaled tli.scaled
#> 1       f1      128.538        20 0.109 1.216      0.918      0.886
#> 2       f2       19.080        13 0.034 0.374      0.995      0.990
#>   rmsea.scaled
#> 1        0.234
#> 2        0.069
# }