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Report the CFA boosting results

Source: R/report_cfa_results.R
report_cfa_results.Rd

Counterpart of report_efa_results for objects returned by cfa_boosting. Side-effect: writes a human-readable report to the console. Return value: an invisible list (class "cfa_boost_report") with the same information structured for programmatic use, including a text field with the formatted lines so any frontend can re-render exactly what the console showed.

Usage

report_cfa_results(res, show_plot = TRUE, print = TRUE)

Arguments

res

A list produced by cfa_boosting(). Expected fields include removed_items, added_covariances, fit_indices, targets_met, iterations, standardized_loadings, factor_correlations, reliability, steps_log, final_syntax.

show_plot

Logical. If TRUE (default), include ASCII charts of RMSEA / CFI / SRMR evolution.

print

Logical. If TRUE (default), write the formatted report to the console. Set to FALSE when only the structured list is needed.

Value

An invisible list with class "cfa_boost_report" - see Details below for fields.

Details

Fields of the returned list:

type

"cfa_boosting"

summary

Single-row list with iterations, n_removed_items, n_added_covariances, targets_all_met

fit_indices

Final-model rmsea, cfi, tli, srmr, chisq, df

targets_met

Logical flags by index from res$targets_met

removed_items

Character vector

added_covariances

Character vector with "x ~~ y" strings

standardized_loadings

Loadings data.frame

factor_correlations

Phi matrix

reliability

Reliability table (composite/AVE/etc.)

steps_log

Per-iteration log

final_syntax

The final lavaan syntax used

text

Character vector - same lines that were printed

See also

cfa_boosting, report_efa_results

Examples

# \donttest{
data(Data_Personality, package = "OptimalFactor")
# Run CFA boosting first to obtain an object suitable for the reporter.
model <- '
F1 =~ PPTQ1 + PPTQ2 + PPTQ3 + PPTQ4 + PPTQ5
F2 =~ PPTQ6 + PPTQ7 + PPTQ8 + PPTQ9 + PPTQ10
F3 =~ PPTQ11 + PPTQ12 + PPTQ13 + PPTQ14 + PPTQ15
'
res <- cfa_boosting(Data_Personality, model,
                    model_config = list(estimator = "MLR", ordered = FALSE),
                    verbose = FALSE)
#> Warning: lavaan->lavaan():  
#>    the first indicator of the following latent variable(s) is a poor item; 
#>    switching to another marker item (to set the metric) to avoid convergence 
#>    problems; use bad.marker.crit = 0 to switch off this behavior: F2 (PPTQ6 
#>    -> PPTQ10)
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lavaan():  
#>    the first indicator of the following latent variable(s) is a poor item; 
#>    switching to another marker item (to set the metric) to avoid convergence 
#>    problems; use bad.marker.crit = 0 to switch off this behavior: F2 (PPTQ6 
#>    -> PPTQ10)
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lavaan():  
#>    the first indicator of the following latent variable(s) is a poor item; 
#>    switching to another marker item (to set the metric) to avoid convergence 
#>    problems; use bad.marker.crit = 0 to switch off this behavior: F2 (PPTQ6 
#>    -> PPTQ10)
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lavaan():  
#>    the first indicator of the following latent variable(s) is a poor item; 
#>    switching to another marker item (to set the metric) to avoid convergence 
#>    problems; use bad.marker.crit = 0 to switch off this behavior: F1 (PPTQ1 
#>    -> PPTQ5)
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lavaan():  
#>    the first indicator of the following latent variable(s) is a poor item; 
#>    switching to another marker item (to set the metric) to avoid convergence 
#>    problems; use bad.marker.crit = 0 to switch off this behavior: F1 (PPTQ1 
#>    -> PPTQ5)
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lavaan():  
#>    the first indicator of the following latent variable(s) is a poor item; 
#>    switching to another marker item (to set the metric) to avoid convergence 
#>    problems; use bad.marker.crit = 0 to switch off this behavior: F1 (PPTQ1 
#>    -> PPTQ5)
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lavaan():  
#>    the first indicator of the following latent variable(s) is a poor item; 
#>    switching to another marker item (to set the metric) to avoid convergence 
#>    problems; use bad.marker.crit = 0 to switch off this behavior: F1 (PPTQ1 
#>    -> PPTQ5)
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lavaan():  
#>    the first indicator of the following latent variable(s) is a poor item; 
#>    switching to another marker item (to set the metric) to avoid convergence 
#>    problems; use bad.marker.crit = 0 to switch off this behavior: F1 (PPTQ1 
#>    -> PPTQ5)
#> Warning: lavaan->lav_object_post_check():  
#>    covariance matrix of latent variables is not positive definite ; use 
#>    lavInspect(fit, "cov.lv") to investigate.
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F2
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F1 F3
#> Warning: lavaan->lav_start_check_cov():  
#>    starting values imply a correlation larger than 1; variables involved are: 
#>    F2 F3

# Pretty print to the console (default).
report_cfa_results(res)
#> 
#> ─────────────────────────────────────────────────────────────────
#> ANÁLISIS FACTORIAL CONFIRMATORIO - REPORTE DE OPTIMIZACIÓN
#> ─────────────────────────────────────────────────────────────────
#> 
#> RESUMEN DEL PROCESO
#> 
#>  Metrica                  Valor
#>  Iteraciones              8    
#>  Items eliminados         5    
#>  Covarianzas agregadas    2    
#>  Cumple todos los targets No   
#> 
#> Items eliminados: PPTQ13, PPTQ8, PPTQ7, PPTQ14, PPTQ4
#> Covarianzas agregadas: PPTQ9 ~~ PPTQ10; PPTQ11 ~~ PPTQ12
#> 
#> 
#> EVOLUCIÓN DEL RMSEA
#> 
#>   Paso  0 [0.083] ████████
#>   Paso  1 [0.093] ███████████
#>   Paso  2 [0.100] █████████████
#>   Paso  3 [0.096] ████████████
#>   Paso  4 [0.093] ███████████
#>   Paso  5 [0.082] ████████
#>   Paso  6 [0.069] █████
#>   Paso  7 [0.067] ████
#>                     └─────┴─────┴─────┴─────┘
#>                      0.05  0.08  0.10  0.13
#> 
#> 
#> EVOLUCIÓN DEL CFI
#> 
#>   Paso  0 [0.687] 
#>   Paso  1 [0.674] 
#>   Paso  2 [0.677] 
#>   Paso  3 [0.732] ██
#>   Paso  4 [0.777] █████
#>   Paso  5 [0.831] █████████
#>   Paso  6 [0.883] ████████████
#>   Paso  7 [0.894] █████████████
#>                     └─────┴─────┴─────┴─────┴─────┘
#>                      0.70  0.80  0.90  0.95  1.00
#> 
#> 
#> EVOLUCIÓN DEL SRMR
#> 
#>   Paso  0 [0.098] ███████████████████
#>   Paso  1 [0.101] ████████████████████
#>   Paso  2 [0.104] ████████████████████
#>   Paso  3 [0.099] ████████████████████
#>   Paso  4 [0.094] ██████████████████
#>   Paso  5 [0.089] █████████████████
#>   Paso  6 [0.085] ████████████████
#>   Paso  7 [0.082] ███████████████
#>                     └─────┴─────┴─────┴─────┘
#>                      0.03  0.05  0.08  0.10
#> 
#> 
#> ÍNDICES DE AJUSTE DEL MODELO FINAL
#> 
#>        Indice Valor    Estado
#>  Chi-cuadrado 43.41          
#>            gl    30          
#>         RMSEA 0.067        OK
#>           CFI 0.894 NO CUMPLE
#>           TLI 0.841          
#>          SRMR 0.082 NO CUMPLE
#> 
#> 
#> CARGAS ESTANDARIZADAS (modelo final)
#> 
#>    lhs    rhs est.std    se pvalue
#> 1   F1  PPTQ1   0.502 0.084  0.000
#> 2   F1  PPTQ2  -0.316 0.120  0.008
#> 3   F1  PPTQ3   0.337 0.115  0.003
#> 4   F1  PPTQ5   0.725 0.099  0.000
#> 5   F2  PPTQ6   0.387 0.113  0.001
#> 6   F2  PPTQ9   0.202 0.107  0.059
#> 7   F2 PPTQ10   0.503 0.160  0.002
#> 8   F3 PPTQ11   0.263 0.119  0.027
#> 9   F3 PPTQ12  -0.278 0.124  0.025
#> 10  F3 PPTQ15   0.486 0.172  0.005
#> 
#> 
#> FIABILIDAD COMPUESTA
#> 
#> $F1
#> 
#> Composite `F1` is composed of observed variables:
#> 	PPTQ1, PPTQ2, PPTQ3, PPTQ5
#> True-score variance is represented by common factor(s):
#> 	F1
#> Total variance of composite `F1` determined from the unrestricted model.
#> The proportion attributable to "true" scores is its model-based estimate of reliability ("omega"):
#> 
#> [1] 0.3
#> 
#> $F2
#> 
#> Composite `F2` is composed of observed variables:
#> 	PPTQ6, PPTQ9, PPTQ10
#> True-score variance is represented by common factor(s):
#> 	F2
#> Total variance of composite `F2` determined from the unrestricted model.
#> The proportion attributable to "true" scores is its model-based estimate of reliability ("omega"):
#> 
#> [1] 0.271
#> 
#> $F3
#> 
#> Composite `F3` is composed of observed variables:
#> 	PPTQ11, PPTQ12, PPTQ15
#> True-score variance is represented by common factor(s):
#> 	F3
#> Total variance of composite `F3` determined from the unrestricted model.
#> The proportion attributable to "true" scores is its model-based estimate of reliability ("omega"):
#> 
#> [1] 0.055
#> 
#> 
#> 
#> MODELO FINAL (sintaxis lavaan)
#> 
#> F1 =~ PPTQ1 + PPTQ2 + PPTQ3 + PPTQ5
#> F2 =~ PPTQ6 + PPTQ9 + PPTQ10
#> F3 =~ PPTQ11 + PPTQ12 + PPTQ15
#> PPTQ9 ~~ PPTQ10
#> PPTQ11 ~~ PPTQ12
#> 
#> ─────────────────────────────────────────────────────────────────

# Capture the structured output without printing - useful inside Shiny
# apps or scripts that need the data programmatically.
rep <- report_cfa_results(res, print = FALSE)
str(rep, max.level = 1)
#> List of 12
#>  $ type                 : chr "cfa_boosting"
#>  $ summary              :List of 4
#>  $ fit_indices          :List of 6
#>  $ targets_met          :List of 4
#>  $ removed_items        : chr [1:5] "PPTQ13" "PPTQ8" "PPTQ7" "PPTQ14" ...
#>  $ added_covariances    : chr [1:2] "PPTQ9 ~~ PPTQ10" "PPTQ11 ~~ PPTQ12"
#>  $ standardized_loadings:Classes ‘lavaan.data.frame’ and 'data.frame':	10 obs. of  5 variables:
#>  $ factor_correlations  :Classes ‘lavaan.data.frame’ and 'data.frame':	3 obs. of  4 variables:
#>  $ reliability          :List of 3
#>  $ steps_log            :'data.frame':	8 obs. of  7 variables:
#>  $ final_syntax         : chr "F1 =~ PPTQ1 + PPTQ2 + PPTQ3 + PPTQ5\nF2 =~ PPTQ6 + PPTQ9 + PPTQ10\nF3 =~ PPTQ11 + PPTQ12 + PPTQ15\nPPTQ9 ~~ PPT"| __truncated__
#>  $ text                 : chr [1:131] "" "─────────────────────────────────────────────────────────────────" "ANÁLISIS FACTORIAL CONFIRMATORIO - REPORTE DE OPTIMIZACIÓN" "─────────────────────────────────────────────────────────────────" ...
#>  - attr(*, "class")= chr [1:2] "cfa_boost_report" "list"
# }