Teoría

Los Modelos de Ecuaciones Estructurales (SEM) son una técnica de análisis de estadística multivariada que permite analizar patrones complejos de relaciones entre variables, realizar comparaciones entre e intra grupos, y validar modelos teóricos y empíricos.

Ejemplo 1. Estudio de Holzinger y Swineford (1939)

Contexto

Holzinger y Swineford realizaron exámenes de habilidad mental a adolescentes de 7º y 8º grado de dos escuelas (Pasteur y Grant-White).

La base de datos está incluida como paquete en R, e incluye las siguientes columnas

  • sex: Género (1=male, 2=female)
  • x1: Percepción visual
  • x2: Juego de cubos
  • x3: Juego con pastillas/especial
  • x4: Comprensión de párrafos
  • x5: Completar oraciones
  • x6: Significado de palabras
  • x7: Sumas aceleradas
  • x8: Conteo acelerado de puntos
  • x9: Discriminación acelerada de mayúsculas rectas y curvas

Se busca identificar las relaciones entre las habilidades visuales (x1, x2, x3), textual (x4, x5, x6) y velocidad (x7, x8, x9) de los adolecentes

Instalar paquetes y llamar librerias

# install.packages("lavaan") #latent variable analisis
# install.packages("lavaanPlot")

library(lavaan)
library(lavaanPlot)

<span style=“color: blue;> Entender la base de datos

df1 <- HolzingerSwineford1939

summary(df1)
##        id             sex            ageyr        agemo       
##  Min.   :  1.0   Min.   :1.000   Min.   :11   Min.   : 0.000  
##  1st Qu.: 82.0   1st Qu.:1.000   1st Qu.:12   1st Qu.: 2.000  
##  Median :163.0   Median :2.000   Median :13   Median : 5.000  
##  Mean   :176.6   Mean   :1.515   Mean   :13   Mean   : 5.375  
##  3rd Qu.:272.0   3rd Qu.:2.000   3rd Qu.:14   3rd Qu.: 8.000  
##  Max.   :351.0   Max.   :2.000   Max.   :16   Max.   :11.000  
##                                                               
##          school        grade             x1               x2       
##  Grant-White:145   Min.   :7.000   Min.   :0.6667   Min.   :2.250  
##  Pasteur    :156   1st Qu.:7.000   1st Qu.:4.1667   1st Qu.:5.250  
##                    Median :7.000   Median :5.0000   Median :6.000  
##                    Mean   :7.477   Mean   :4.9358   Mean   :6.088  
##                    3rd Qu.:8.000   3rd Qu.:5.6667   3rd Qu.:6.750  
##                    Max.   :8.000   Max.   :8.5000   Max.   :9.250  
##                    NA's   :1                                       
##        x3              x4              x5              x6        
##  Min.   :0.250   Min.   :0.000   Min.   :1.000   Min.   :0.1429  
##  1st Qu.:1.375   1st Qu.:2.333   1st Qu.:3.500   1st Qu.:1.4286  
##  Median :2.125   Median :3.000   Median :4.500   Median :2.0000  
##  Mean   :2.250   Mean   :3.061   Mean   :4.341   Mean   :2.1856  
##  3rd Qu.:3.125   3rd Qu.:3.667   3rd Qu.:5.250   3rd Qu.:2.7143  
##  Max.   :4.500   Max.   :6.333   Max.   :7.000   Max.   :6.1429  
##                                                                  
##        x7              x8               x9       
##  Min.   :1.304   Min.   : 3.050   Min.   :2.778  
##  1st Qu.:3.478   1st Qu.: 4.850   1st Qu.:4.750  
##  Median :4.087   Median : 5.500   Median :5.417  
##  Mean   :4.186   Mean   : 5.527   Mean   :5.374  
##  3rd Qu.:4.913   3rd Qu.: 6.100   3rd Qu.:6.083  
##  Max.   :7.435   Max.   :10.000   Max.   :9.250  
## 
head(df1)
##   id sex ageyr agemo  school grade       x1   x2    x3       x4   x5        x6
## 1  1   1    13     1 Pasteur     7 3.333333 7.75 0.375 2.333333 5.75 1.2857143
## 2  2   2    13     7 Pasteur     7 5.333333 5.25 2.125 1.666667 3.00 1.2857143
## 3  3   2    13     1 Pasteur     7 4.500000 5.25 1.875 1.000000 1.75 0.4285714
## 4  4   1    13     2 Pasteur     7 5.333333 7.75 3.000 2.666667 4.50 2.4285714
## 5  5   2    12     2 Pasteur     7 4.833333 4.75 0.875 2.666667 4.00 2.5714286
## 6  6   2    14     1 Pasteur     7 5.333333 5.00 2.250 1.000000 3.00 0.8571429
##         x7   x8       x9
## 1 3.391304 5.75 6.361111
## 2 3.782609 6.25 7.916667
## 3 3.260870 3.90 4.416667
## 4 3.000000 5.30 4.861111
## 5 3.695652 6.30 5.916667
## 6 4.347826 6.65 7.500000

<span style=“color: blue;> Tipos de formulas

  1. Regresión (~) Variable que depende de otras
  2. Variables Latentes (=~) No se observa, se infiere.
  3. Variablew y Covarianzas (~~) Relaciones entre variables latentes y observada (Varianza: entre sí misma, Covarianza: entre otras).
  4. Intercepto (~1) Valor esperado cuando las demás variables son cero.

<span style=“color: blue;> Estructurar el modelo 

modelo1 <- ' # Regresiones
            # Variables Latentes  
            visual =~ x1 + x2 + x3
            textual =~ x4+ x5 +x6
            velocidad =~ x7 + x8 +x9
            # Varianzas y Covarianzas
            visual ~~ visual
            textual ~~ textual
            velocidad ~~ velocidad
            visual ~~ textual + velocidad
            textual ~~ velocidad
            # Intercepto
          '

<span style=“color: blue;> Generar el Análisis Factorial Confrimatorio 

cfa1 <- sem(modelo1, data=df1)
summary(cfa1)
## lavaan 0.6-19 ended normally after 35 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        21
## 
##   Number of observations                           301
## 
## Model Test User Model:
##                                                       
##   Test statistic                                85.306
##   Degrees of freedom                                24
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   visual =~                                           
##     x1                1.000                           
##     x2                0.554    0.100    5.554    0.000
##     x3                0.729    0.109    6.685    0.000
##   textual =~                                          
##     x4                1.000                           
##     x5                1.113    0.065   17.014    0.000
##     x6                0.926    0.055   16.703    0.000
##   velocidad =~                                        
##     x7                1.000                           
##     x8                1.180    0.165    7.152    0.000
##     x9                1.082    0.151    7.155    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   visual ~~                                           
##     textual           0.408    0.074    5.552    0.000
##     velocidad         0.262    0.056    4.660    0.000
##   textual ~~                                          
##     velocidad         0.173    0.049    3.518    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     visual            0.809    0.145    5.564    0.000
##     textual           0.979    0.112    8.737    0.000
##     velocidad         0.384    0.086    4.451    0.000
##    .x1                0.549    0.114    4.833    0.000
##    .x2                1.134    0.102   11.146    0.000
##    .x3                0.844    0.091    9.317    0.000
##    .x4                0.371    0.048    7.779    0.000
##    .x5                0.446    0.058    7.642    0.000
##    .x6                0.356    0.043    8.277    0.000
##    .x7                0.799    0.081    9.823    0.000
##    .x8                0.488    0.074    6.573    0.000
##    .x9                0.566    0.071    8.003    0.000
lavaanPlot(cfa1, coef=TRUE, cov=TRUE)

Actividad 3. Aplicación de modelos de ecuaciones estructurales

library(readxl)
library(lavaan)
library(lavaanPlot)
# file.choose()
df3 <- read_excel("/Users/jenaromtzg/Desktop/Generación de Escenarios Futuros/R/Semana 2/Datos_SEM_Eng.xlsx")
summary(df3)
##        ID             GEN             EXPER            EDAD      
##  Min.   :  1.0   Min.   :0.0000   Min.   : 0.00   Min.   :22.00  
##  1st Qu.: 56.5   1st Qu.:0.0000   1st Qu.:15.00   1st Qu.:37.50  
##  Median :112.0   Median :1.0000   Median :20.00   Median :44.00  
##  Mean   :112.0   Mean   :0.5919   Mean   :21.05   Mean   :43.95  
##  3rd Qu.:167.5   3rd Qu.:1.0000   3rd Qu.:27.50   3rd Qu.:51.00  
##  Max.   :223.0   Max.   :1.0000   Max.   :50.00   Max.   :72.00  
##      RPD01           RPD02          RPD03           RPD05           RPD06      
##  Min.   :1.000   Min.   :1.00   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:3.000   1st Qu.:3.00   1st Qu.:3.000   1st Qu.:3.000   1st Qu.:3.000  
##  Median :5.000   Median :4.00   Median :5.000   Median :5.000   Median :5.000  
##  Mean   :4.596   Mean   :4.09   Mean   :4.789   Mean   :4.327   Mean   :4.798  
##  3rd Qu.:6.000   3rd Qu.:6.00   3rd Qu.:7.000   3rd Qu.:6.000   3rd Qu.:7.000  
##  Max.   :7.000   Max.   :7.00   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##      RPD07           RPD08           RPD09           RPD10      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:2.000   1st Qu.:3.000   1st Qu.:3.000   1st Qu.:2.500  
##  Median :4.000   Median :5.000   Median :5.000   Median :5.000  
##  Mean   :3.794   Mean   :4.735   Mean   :4.466   Mean   :4.435  
##  3rd Qu.:5.500   3rd Qu.:7.000   3rd Qu.:6.000   3rd Qu.:6.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##      RRE02           RRE03           RRE04           RRE05           RRE06    
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.0  
##  1st Qu.:5.000   1st Qu.:5.000   1st Qu.:5.000   1st Qu.:5.000   1st Qu.:4.0  
##  Median :6.000   Median :6.000   Median :6.000   Median :6.000   Median :6.0  
##  Mean   :5.691   Mean   :5.534   Mean   :5.668   Mean   :5.623   Mean   :5.3  
##  3rd Qu.:7.000   3rd Qu.:7.000   3rd Qu.:7.000   3rd Qu.:7.000   3rd Qu.:7.0  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.0  
##      RRE07           RRE10           RMA02           RMA03      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:4.000   1st Qu.:5.000   1st Qu.:3.000   1st Qu.:3.000  
##  Median :6.000   Median :6.000   Median :4.000   Median :5.000  
##  Mean   :5.305   Mean   :5.664   Mean   :4.215   Mean   :4.377  
##  3rd Qu.:7.000   3rd Qu.:7.000   3rd Qu.:6.000   3rd Qu.:6.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##      RMA04           RMA05           RMA06           RMA07      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:3.000   1st Qu.:3.000   1st Qu.:5.000   1st Qu.:4.000  
##  Median :5.000   Median :5.000   Median :6.000   Median :5.000  
##  Mean   :4.686   Mean   :4.637   Mean   :5.511   Mean   :4.767  
##  3rd Qu.:6.000   3rd Qu.:6.000   3rd Qu.:7.000   3rd Qu.:6.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##      RMA08           RMA09           RMA10          RCO02           RCO03      
##  Min.   :1.000   Min.   :1.000   Min.   :1.00   Min.   :1.000   Min.   :1.000  
##  1st Qu.:4.000   1st Qu.:3.000   1st Qu.:3.00   1st Qu.:5.000   1st Qu.:5.000  
##  Median :5.000   Median :5.000   Median :5.00   Median :6.000   Median :6.000  
##  Mean   :4.942   Mean   :4.614   Mean   :4.43   Mean   :5.336   Mean   :5.574  
##  3rd Qu.:6.500   3rd Qu.:6.000   3rd Qu.:6.00   3rd Qu.:7.000   3rd Qu.:7.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.00   Max.   :7.000   Max.   :7.000  
##      RCO04           RCO05           RCO06           RCO07      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:5.000   1st Qu.:5.000   1st Qu.:5.000   1st Qu.:5.000  
##  Median :6.000   Median :6.000   Median :6.000   Median :6.000  
##  Mean   :5.704   Mean   :5.668   Mean   :5.619   Mean   :5.632  
##  3rd Qu.:7.000   3rd Qu.:7.000   3rd Qu.:7.000   3rd Qu.:7.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##       EN01            EN02            EN04            EN05      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:3.000   1st Qu.:4.000   1st Qu.:4.000   1st Qu.:4.000  
##  Median :5.000   Median :6.000   Median :5.000   Median :5.000  
##  Mean   :4.717   Mean   :5.004   Mean   :4.883   Mean   :4.928  
##  3rd Qu.:6.000   3rd Qu.:7.000   3rd Qu.:6.000   3rd Qu.:6.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##       EN06            EN07            EN08           EVI01      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :0.000  
##  1st Qu.:3.000   1st Qu.:3.000   1st Qu.:4.000   1st Qu.:4.000  
##  Median :5.000   Median :5.000   Median :5.000   Median :5.000  
##  Mean   :4.767   Mean   :4.578   Mean   :4.776   Mean   :5.013  
##  3rd Qu.:6.000   3rd Qu.:6.000   3rd Qu.:6.000   3rd Qu.:6.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##      EVI02           EVI03           EDE01           EDE02      
##  Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
##  1st Qu.:4.000   1st Qu.:4.000   1st Qu.:5.000   1st Qu.:5.000  
##  Median :6.000   Median :6.000   Median :6.000   Median :6.000  
##  Mean   :5.076   Mean   :4.973   Mean   :5.305   Mean   :5.543  
##  3rd Qu.:6.000   3rd Qu.:6.000   3rd Qu.:7.000   3rd Qu.:7.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##      EDE03           EAB01           EAB02           EAB03      
##  Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
##  1st Qu.:6.000   1st Qu.:5.000   1st Qu.:5.000   1st Qu.:5.000  
##  Median :7.000   Median :6.000   Median :6.000   Median :6.000  
##  Mean   :6.135   Mean   :5.605   Mean   :5.821   Mean   :5.363  
##  3rd Qu.:7.000   3rd Qu.:7.000   3rd Qu.:7.000   3rd Qu.:7.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000
str(df3)
## tibble [223 × 51] (S3: tbl_df/tbl/data.frame)
##  $ ID   : num [1:223] 1 2 3 4 5 6 7 8 9 10 ...
##  $ GEN  : num [1:223] 1 1 1 1 1 0 0 1 1 1 ...
##  $ EXPER: num [1:223] 22 22 30 17 23 31 26 30 15 15 ...
##  $ EDAD : num [1:223] 45 44 52 41 51 52 53 48 40 38 ...
##  $ RPD01: num [1:223] 5 4 7 5 7 3 5 6 4 2 ...
##  $ RPD02: num [1:223] 1 4 7 5 6 4 5 7 4 3 ...
##  $ RPD03: num [1:223] 3 6 7 1 7 5 4 6 4 2 ...
##  $ RPD05: num [1:223] 2 5 7 1 6 4 4 7 4 3 ...
##  $ RPD06: num [1:223] 3 3 7 3 7 3 5 2 6 7 ...
##  $ RPD07: num [1:223] 1 2 6 5 6 5 6 5 4 1 ...
##  $ RPD08: num [1:223] 3 3 7 3 7 4 6 2 5 3 ...
##  $ RPD09: num [1:223] 2 4 7 2 6 4 7 4 4 2 ...
##  $ RPD10: num [1:223] 4 4 7 2 6 4 7 1 6 2 ...
##  $ RRE02: num [1:223] 6 6 7 6 7 5 7 5 6 7 ...
##  $ RRE03: num [1:223] 6 6 7 6 7 4 7 4 4 7 ...
##  $ RRE04: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
##  $ RRE05: num [1:223] 6 6 7 6 7 5 7 4 6 7 ...
##  $ RRE06: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
##  $ RRE07: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
##  $ RRE10: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
##  $ RMA02: num [1:223] 4 6 4 3 4 7 5 2 6 7 ...
##  $ RMA03: num [1:223] 5 6 5 4 4 7 5 1 2 7 ...
##  $ RMA04: num [1:223] 5 5 6 4 4 5 5 1 4 7 ...
##  $ RMA05: num [1:223] 5 5 6 4 4 6 5 3 4 7 ...
##  $ RMA06: num [1:223] 6 6 7 6 5 4 5 7 6 7 ...
##  $ RMA07: num [1:223] 4 6 6 5 4 5 7 4 6 7 ...
##  $ RMA08: num [1:223] 5 6 4 4 4 6 6 4 2 7 ...
##  $ RMA09: num [1:223] 3 5 4 3 5 4 5 2 4 7 ...
##  $ RMA10: num [1:223] 7 5 5 4 5 5 6 4 3 7 ...
##  $ RCO02: num [1:223] 7 7 7 5 7 6 7 7 3 7 ...
##  $ RCO03: num [1:223] 7 7 7 5 7 5 7 7 3 7 ...
##  $ RCO04: num [1:223] 7 7 7 6 7 4 7 7 3 7 ...
##  $ RCO05: num [1:223] 7 7 7 6 7 4 7 7 3 7 ...
##  $ RCO06: num [1:223] 7 7 7 6 7 4 7 7 4 7 ...
##  $ RCO07: num [1:223] 5 7 7 6 7 4 7 7 7 7 ...
##  $ EN01 : num [1:223] 6 6 7 4 6 4 7 7 4 7 ...
##  $ EN02 : num [1:223] 7 6 7 4 6 4 7 7 4 7 ...
##  $ EN04 : num [1:223] 6 6 7 4 6 4 7 6 4 7 ...
##  $ EN05 : num [1:223] 5 5 7 5 6 5 7 6 4 7 ...
##  $ EN06 : num [1:223] 5 5 7 5 6 3 7 5 5 7 ...
##  $ EN07 : num [1:223] 5 5 7 2 6 4 7 4 4 7 ...
##  $ EN08 : num [1:223] 6 5 7 5 6 4 7 4 4 7 ...
##  $ EVI01: num [1:223] 6 5 7 5 6 4 7 6 6 0 ...
##  $ EVI02: num [1:223] 6 5 7 6 6 4 6 5 5 1 ...
##  $ EVI03: num [1:223] 6 6 6 7 6 4 6 6 7 0 ...
##  $ EDE01: num [1:223] 6 6 6 5 7 6 7 7 7 1 ...
##  $ EDE02: num [1:223] 7 6 7 6 7 5 7 7 7 5 ...
##  $ EDE03: num [1:223] 7 7 7 7 7 5 7 7 7 6 ...
##  $ EAB01: num [1:223] 7 7 7 6 7 5 7 7 7 0 ...
##  $ EAB02: num [1:223] 7 7 7 6 7 5 7 2 5 1 ...
##  $ EAB03: num [1:223] 6 5 6 5 6 5 7 3 5 0 ...
head(df3)
## # A tibble: 6 × 51
##      ID   GEN EXPER  EDAD RPD01 RPD02 RPD03 RPD05 RPD06 RPD07 RPD08 RPD09 RPD10
##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1     1     1    22    45     5     1     3     2     3     1     3     2     4
## 2     2     1    22    44     4     4     6     5     3     2     3     4     4
## 3     3     1    30    52     7     7     7     7     7     6     7     7     7
## 4     4     1    17    41     5     5     1     1     3     5     3     2     2
## 5     5     1    23    51     7     6     7     6     7     6     7     6     6
## 6     6     0    31    52     3     4     5     4     3     5     4     4     4
## # … with 38 more variables: RRE02 <dbl>, RRE03 <dbl>, RRE04 <dbl>, RRE05 <dbl>,
## #   RRE06 <dbl>, RRE07 <dbl>, RRE10 <dbl>, RMA02 <dbl>, RMA03 <dbl>,
## #   RMA04 <dbl>, RMA05 <dbl>, RMA06 <dbl>, RMA07 <dbl>, RMA08 <dbl>,
## #   RMA09 <dbl>, RMA10 <dbl>, RCO02 <dbl>, RCO03 <dbl>, RCO04 <dbl>,
## #   RCO05 <dbl>, RCO06 <dbl>, RCO07 <dbl>, EN01 <dbl>, EN02 <dbl>, EN04 <dbl>,
## #   EN05 <dbl>, EN06 <dbl>, EN07 <dbl>, EN08 <dbl>, EVI01 <dbl>, EVI02 <dbl>,
## #   EVI03 <dbl>, EDE01 <dbl>, EDE02 <dbl>, EDE03 <dbl>, EAB01 <dbl>, …

<span style=“color: blue;> Estructurar el modelo 

modelo31 <- ' # Regresiones
            # Variables Latentes  
            desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
            relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
            dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
            control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
            recuperacion =~ desapego + relajacion + dominio + control
            # Varianzas y Covarianzas
            desapego ~~ desapego
            relajacion ~~ relajacion
            dominio ~~ dominio
            control ~~ control
            # Intercepto
          '

<span style=“color: blue;> Generar el Análisis Factorial Confrimatorio 

cfa31 <- sem(modelo31, data=df3)
summary(cfa31)
## lavaan 0.6-19 ended normally after 47 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        66
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                              1221.031
##   Degrees of freedom                               430
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   desapego =~                                         
##     RPD01             1.000                           
##     RPD02             1.206    0.082   14.780    0.000
##     RPD03             1.143    0.085   13.374    0.000
##     RPD05             1.312    0.086   15.244    0.000
##     RPD06             1.088    0.089   12.266    0.000
##     RPD07             1.229    0.085   14.440    0.000
##     RPD08             1.164    0.087   13.447    0.000
##     RPD09             1.317    0.087   15.153    0.000
##     RPD10             1.346    0.088   15.258    0.000
##   relajacion =~                                       
##     RRE02             1.000                           
##     RRE03             1.120    0.065   17.227    0.000
##     RRE04             1.025    0.058   17.713    0.000
##     RRE05             1.055    0.056   18.758    0.000
##     RRE06             1.245    0.074   16.869    0.000
##     RRE07             1.117    0.071   15.689    0.000
##     RRE10             0.815    0.067   12.120    0.000
##   dominio =~                                          
##     RMA02             1.000                           
##     RMA03             1.155    0.096   12.079    0.000
##     RMA04             1.178    0.089   13.274    0.000
##     RMA05             1.141    0.087   13.072    0.000
##     RMA06             0.645    0.075    8.597    0.000
##     RMA07             1.103    0.084   13.061    0.000
##     RMA08             1.109    0.085   12.994    0.000
##     RMA09             1.028    0.084   12.246    0.000
##     RMA10             1.055    0.088   12.044    0.000
##   control =~                                          
##     RCO02             1.000                           
##     RCO03             0.948    0.049   19.182    0.000
##     RCO04             0.796    0.044   18.110    0.000
##     RCO05             0.818    0.043   18.990    0.000
##     RCO06             0.834    0.046   18.216    0.000
##     RCO07             0.835    0.046   18.057    0.000
##   recuperacion =~                                     
##     desapego          1.000                           
##     relajacion        1.149    0.131    8.787    0.000
##     dominio           0.858    0.129    6.666    0.000
##     control           1.341    0.156    8.605    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .desapego          0.943    0.152    6.207    0.000
##    .relajacion        0.333    0.089    3.757    0.000
##    .dominio           1.260    0.212    5.942    0.000
##    .control           0.900    0.159    5.666    0.000
##    .RPD01             1.172    0.120    9.782    0.000
##    .RPD02             0.999    0.108    9.228    0.000
##    .RPD03             1.441    0.148    9.733    0.000
##    .RPD05             0.987    0.110    8.964    0.000
##    .RPD06             1.817    0.182    9.967    0.000
##    .RPD07             1.173    0.125    9.383    0.000
##    .RPD08             1.460    0.150    9.714    0.000
##    .RPD09             1.032    0.114    9.021    0.000
##    .RPD10             1.034    0.115    8.955    0.000
##    .RRE02             0.626    0.068    9.274    0.000
##    .RRE03             0.653    0.073    9.011    0.000
##    .RRE04             0.481    0.055    8.794    0.000
##    .RRE05             0.374    0.046    8.153    0.000
##    .RRE06             0.886    0.097    9.149    0.000
##    .RRE07             0.950    0.100    9.505    0.000
##    .RRE10             1.137    0.113   10.093    0.000
##    .RMA02             1.740    0.175    9.931    0.000
##    .RMA03             1.485    0.155    9.575    0.000
##    .RMA04             0.855    0.097    8.772    0.000
##    .RMA05             0.899    0.100    8.967    0.000
##    .RMA06             1.631    0.159   10.281    0.000
##    .RMA07             0.845    0.094    8.977    0.000
##    .RMA08             0.886    0.098    9.034    0.000
##    .RMA09             1.094    0.115    9.500    0.000
##    .RMA10             1.259    0.131    9.590    0.000
##    .RCO02             0.983    0.105    9.379    0.000
##    .RCO03             0.484    0.058    8.391    0.000
##    .RCO04             0.462    0.052    8.963    0.000
##    .RCO05             0.382    0.045    8.513    0.000
##    .RCO06             0.494    0.055    8.917    0.000
##    .RCO07             0.515    0.057    8.985    0.000
##     recuperacion      0.978    0.202    4.833    0.000
lavaanPlot(cfa31, coef=TRUE, cov=TRUE)
modelo32 <- ' # Regresiones
            # Variables Latentes  
            energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
  
            # Varianzas y Covarianzas
            energia ~~ energia

            # Intercepto
          '

<span style=“color: blue;> Generar el Análisis Factorial Confrimatorio 

cfa32 <- sem(modelo32, data=df3)
summary(cfa32)
## lavaan 0.6-19 ended normally after 32 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        14
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                                47.222
##   Degrees of freedom                                14
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   energia =~                                          
##     EN01              1.000                           
##     EN02              1.029    0.044   23.192    0.000
##     EN04              0.999    0.044   22.583    0.000
##     EN05              0.999    0.042   23.649    0.000
##     EN06              0.986    0.042   23.722    0.000
##     EN07              1.049    0.046   22.856    0.000
##     EN08              1.036    0.043   24.173    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     energia           2.801    0.327    8.565    0.000
##    .EN01              0.711    0.074    9.651    0.000
##    .EN02              0.444    0.049    9.012    0.000
##    .EN04              0.481    0.052    9.214    0.000
##    .EN05              0.375    0.042    8.830    0.000
##    .EN06              0.359    0.041    8.798    0.000
##    .EN07              0.499    0.055    9.129    0.000
##    .EN08              0.353    0.041    8.580    0.000
lavaanPlot(cfa32, coef=TRUE, cov=TRUE)
summary(cfa32, fit.measures=TRUE)
## lavaan 0.6-19 ended normally after 32 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        14
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                                47.222
##   Degrees of freedom                                14
##   P-value (Chi-square)                           0.000
## 
## Model Test Baseline Model:
## 
##   Test statistic                              2324.436
##   Degrees of freedom                                21
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    0.986
##   Tucker-Lewis Index (TLI)                       0.978
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -2017.154
##   Loglikelihood unrestricted model (H1)      -1993.543
##                                                       
##   Akaike (AIC)                                4062.308
##   Bayesian (BIC)                              4110.008
##   Sample-size adjusted Bayesian (SABIC)       4065.641
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.103
##   90 Percent confidence interval - lower         0.072
##   90 Percent confidence interval - upper         0.136
##   P-value H_0: RMSEA <= 0.050                    0.004
##   P-value H_0: RMSEA >= 0.080                    0.892
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.012
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   energia =~                                          
##     EN01              1.000                           
##     EN02              1.029    0.044   23.192    0.000
##     EN04              0.999    0.044   22.583    0.000
##     EN05              0.999    0.042   23.649    0.000
##     EN06              0.986    0.042   23.722    0.000
##     EN07              1.049    0.046   22.856    0.000
##     EN08              1.036    0.043   24.173    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     energia           2.801    0.327    8.565    0.000
##    .EN01              0.711    0.074    9.651    0.000
##    .EN02              0.444    0.049    9.012    0.000
##    .EN04              0.481    0.052    9.214    0.000
##    .EN05              0.375    0.042    8.830    0.000
##    .EN06              0.359    0.041    8.798    0.000
##    .EN07              0.499    0.055    9.129    0.000
##    .EN08              0.353    0.041    8.580    0.000
# Revisar los valores de comparative fit index y tucker lewis index
# Excelente si es >= 0.95, aceptable entre 0.9 y 0.95, debieciente menor a 0.90
modelo33 <- ' # Regresiones
            # Variables Latentes  
            vigor =~ EVI01 + EVI02 + EVI03
            dedicacion =~ EDE01 + EDE02 + EDE03
            absorcion =~ EAB01 + EAB02 + EAB03
            
            # Varianzas y Covarianzas
            vigor ~~ vigor
            absorcion ~~ absorcion
            dedicacion ~~ dedicacion
            vigor ~~ dedicacion + absorcion
            dedicacion ~~ absorcion
            # Intercepto
          '

<span style=“color: blue;> Generar el Análisis Factorial Confrimatorio 

cfa33 <- sem(modelo33, data=df3)
summary(cfa33)
## lavaan 0.6-19 ended normally after 44 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        21
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                               271.168
##   Degrees of freedom                                24
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   vigor =~                                            
##     EVI01             1.000                           
##     EVI02             0.986    0.028   35.166    0.000
##     EVI03             0.995    0.049   20.456    0.000
##   dedicacion =~                                       
##     EDE01             1.000                           
##     EDE02             0.914    0.035   26.126    0.000
##     EDE03             0.583    0.037   15.913    0.000
##   absorcion =~                                        
##     EAB01             1.000                           
##     EAB02             0.708    0.051   13.891    0.000
##     EAB03             0.732    0.063   11.644    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   vigor ~~                                            
##     dedicacion        2.754    0.293    9.404    0.000
##     absorcion         2.125    0.247    8.600    0.000
##   dedicacion ~~                                       
##     absorcion         2.728    0.293    9.311    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     vigor             2.836    0.289    9.811    0.000
##     absorcion         2.592    0.301    8.615    0.000
##     dedicacion        3.448    0.367    9.399    0.000
##    .EVI01             0.200    0.040    4.947    0.000
##    .EVI02             0.220    0.041    5.437    0.000
##    .EVI03             1.220    0.125    9.772    0.000
##    .EDE01             0.405    0.066    6.130    0.000
##    .EDE02             0.495    0.066    7.521    0.000
##    .EDE03             0.829    0.084    9.869    0.000
##    .EAB01             0.481    0.100    4.816    0.000
##    .EAB02             1.010    0.109    9.271    0.000
##    .EAB03             1.711    0.175    9.764    0.000
lavaanPlot(cfa33, coef=TRUE, cov=TRUE)
summary(cfa33, fit.measures=TRUE)
## lavaan 0.6-19 ended normally after 44 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        21
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                               271.168
##   Degrees of freedom                                24
##   P-value (Chi-square)                           0.000
## 
## Model Test Baseline Model:
## 
##   Test statistic                              2254.214
##   Degrees of freedom                                36
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    0.889
##   Tucker-Lewis Index (TLI)                       0.833
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -2965.082
##   Loglikelihood unrestricted model (H1)      -2829.498
##                                                       
##   Akaike (AIC)                                5972.164
##   Bayesian (BIC)                              6043.715
##   Sample-size adjusted Bayesian (SABIC)       5977.163
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.215
##   90 Percent confidence interval - lower         0.192
##   90 Percent confidence interval - upper         0.238
##   P-value H_0: RMSEA <= 0.050                    0.000
##   P-value H_0: RMSEA >= 0.080                    1.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.070
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   vigor =~                                            
##     EVI01             1.000                           
##     EVI02             0.986    0.028   35.166    0.000
##     EVI03             0.995    0.049   20.456    0.000
##   dedicacion =~                                       
##     EDE01             1.000                           
##     EDE02             0.914    0.035   26.126    0.000
##     EDE03             0.583    0.037   15.913    0.000
##   absorcion =~                                        
##     EAB01             1.000                           
##     EAB02             0.708    0.051   13.891    0.000
##     EAB03             0.732    0.063   11.644    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   vigor ~~                                            
##     dedicacion        2.754    0.293    9.404    0.000
##     absorcion         2.125    0.247    8.600    0.000
##   dedicacion ~~                                       
##     absorcion         2.728    0.293    9.311    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     vigor             2.836    0.289    9.811    0.000
##     absorcion         2.592    0.301    8.615    0.000
##     dedicacion        3.448    0.367    9.399    0.000
##    .EVI01             0.200    0.040    4.947    0.000
##    .EVI02             0.220    0.041    5.437    0.000
##    .EVI03             1.220    0.125    9.772    0.000
##    .EDE01             0.405    0.066    6.130    0.000
##    .EDE02             0.495    0.066    7.521    0.000
##    .EDE03             0.829    0.084    9.869    0.000
##    .EAB01             0.481    0.100    4.816    0.000
##    .EAB02             1.010    0.109    9.271    0.000
##    .EAB03             1.711    0.175    9.764    0.000
# Revisar los valores de comparative fit index y tucker lewis index
# Excelente si es >= 0.95, aceptable entre 0.9 y 0.95, debieciente menor a 0.90
modelo34 <- ' # Regresiones
            # Variables Latentes  
            desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
            relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
            dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
            control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
            recuperacion =~ desapego + relajacion + dominio + control
            energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
            vigor =~ EVI01 + EVI02 + EVI03
            dedicacion =~ EDE01 + EDE02 + EDE03
            absorcion =~ EAB01 + EAB02 + EAB03
            
            # Varianzas y Covarianzas
            desapego ~~ desapego
            relajacion ~~ relajacion
            dominio ~~ dominio
            control ~~ control
            energia ~~ energia
            vigor ~~ vigor
            absorcion ~~ absorcion
            dedicacion ~~ dedicacion
            vigor ~~ dedicacion + absorcion
            dedicacion ~~ absorcion
            recuperacion  ~~ energia + vigor + dedicacion + absorcion
            energia ~~ vigor + dedicacion + absorcion
            
            # Intercepto
          '

<span style=“color: blue;> Generar el Análisis Factorial Confrimatorio 

cfa34 <- sem(modelo34, data=df3)
summary(cfa34)
## lavaan 0.6-19 ended normally after 90 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       108
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                              2445.310
##   Degrees of freedom                              1020
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   desapego =~                                         
##     RPD01             1.000                           
##     RPD02             1.209    0.081   14.858    0.000
##     RPD03             1.144    0.085   13.413    0.000
##     RPD05             1.313    0.086   15.311    0.000
##     RPD06             1.083    0.089   12.218    0.000
##     RPD07             1.229    0.085   14.481    0.000
##     RPD08             1.157    0.086   13.376    0.000
##     RPD09             1.316    0.087   15.162    0.000
##     RPD10             1.343    0.088   15.247    0.000
##   relajacion =~                                       
##     RRE02             1.000                           
##     RRE03             1.121    0.065   17.303    0.000
##     RRE04             1.020    0.058   17.611    0.000
##     RRE05             1.051    0.056   18.690    0.000
##     RRE06             1.245    0.074   16.916    0.000
##     RRE07             1.122    0.071   15.848    0.000
##     RRE10             0.815    0.067   12.147    0.000
##   dominio =~                                          
##     RMA02             1.000                           
##     RMA03             1.152    0.096   12.038    0.000
##     RMA04             1.178    0.089   13.262    0.000
##     RMA05             1.141    0.087   13.054    0.000
##     RMA06             0.648    0.075    8.623    0.000
##     RMA07             1.104    0.085   13.062    0.000
##     RMA08             1.110    0.085   13.002    0.000
##     RMA09             1.030    0.084   12.257    0.000
##     RMA10             1.056    0.088   12.047    0.000
##   control =~                                          
##     RCO02             1.000                           
##     RCO03             0.946    0.049   19.158    0.000
##     RCO04             0.794    0.044   18.081    0.000
##     RCO05             0.815    0.043   18.912    0.000
##     RCO06             0.837    0.046   18.395    0.000
##     RCO07             0.837    0.046   18.199    0.000
##   recuperacion =~                                     
##     desapego          1.000                           
##     relajacion        1.071    0.121    8.858    0.000
##     dominio           0.900    0.129    6.965    0.000
##     control           1.421    0.157    9.066    0.000
##   energia =~                                          
##     EN01              1.000                           
##     EN02              1.026    0.044   23.558    0.000
##     EN04              0.996    0.043   22.912    0.000
##     EN05              0.994    0.042   23.892    0.000
##     EN06              0.981    0.041   23.944    0.000
##     EN07              1.044    0.045   23.105    0.000
##     EN08              1.031    0.042   24.449    0.000
##   vigor =~                                            
##     EVI01             1.000                           
##     EVI02             0.978    0.027   35.896    0.000
##     EVI03             0.990    0.048   20.656    0.000
##   dedicacion =~                                       
##     EDE01             1.000                           
##     EDE02             0.913    0.035   26.219    0.000
##     EDE03             0.580    0.037   15.851    0.000
##   absorcion =~                                        
##     EAB01             1.000                           
##     EAB02             0.707    0.051   13.915    0.000
##     EAB03             0.730    0.063   11.619    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   vigor ~~                                            
##     dedicacion        2.767    0.293    9.427    0.000
##     absorcion         2.132    0.248    8.613    0.000
##   dedicacion ~~                                       
##     absorcion         2.731    0.293    9.316    0.000
##   recuperacion ~~                                     
##     energia           1.367    0.197    6.938    0.000
##     vigor             1.007    0.165    6.098    0.000
##     dedicacion        1.049    0.179    5.852    0.000
##     absorcion         0.796    0.151    5.281    0.000
##   energia ~~                                          
##     vigor             2.045    0.249    8.223    0.000
##     dedicacion        1.852    0.259    7.139    0.000
##     absorcion         1.340    0.220    6.091    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .desapego          0.951    0.149    6.400    0.000
##    .relajacion        0.510    0.085    6.021    0.000
##    .dominio           1.191    0.200    5.958    0.000
##    .control           0.699    0.125    5.583    0.000
##     energia           2.823    0.327    8.623    0.000
##     vigor             2.859    0.289    9.900    0.000
##     absorcion         2.595    0.301    8.628    0.000
##     dedicacion        3.458    0.367    9.424    0.000
##    .RPD01             1.169    0.120    9.782    0.000
##    .RPD02             0.984    0.107    9.204    0.000
##    .RPD03             1.435    0.147    9.730    0.000
##    .RPD05             0.973    0.109    8.940    0.000
##    .RPD06             1.835    0.184    9.979    0.000
##    .RPD07             1.166    0.124    9.378    0.000
##    .RPD08             1.485    0.152    9.739    0.000
##    .RPD09             1.036    0.115    9.034    0.000
##    .RPD10             1.044    0.116    8.982    0.000
##    .RRE02             0.623    0.067    9.253    0.000
##    .RRE03             0.646    0.072    8.974    0.000
##    .RRE04             0.494    0.056    8.837    0.000
##    .RRE05             0.384    0.047    8.203    0.000
##    .RRE06             0.882    0.097    9.126    0.000
##    .RRE07             0.929    0.098    9.458    0.000
##    .RRE10             1.134    0.112   10.086    0.000
##    .RMA02             1.742    0.175    9.935    0.000
##    .RMA03             1.500    0.156    9.595    0.000
##    .RMA04             0.857    0.098    8.786    0.000
##    .RMA05             0.904    0.101    8.985    0.000
##    .RMA06             1.626    0.158   10.280    0.000
##    .RMA07             0.843    0.094    8.978    0.000
##    .RMA08             0.881    0.098    9.029    0.000
##    .RMA09             1.089    0.115    9.498    0.000
##    .RMA10             1.256    0.131    9.591    0.000
##    .RCO02             0.980    0.104    9.394    0.000
##    .RCO03             0.493    0.058    8.473    0.000
##    .RCO04             0.468    0.052    9.019    0.000
##    .RCO05             0.393    0.046    8.620    0.000
##    .RCO06             0.479    0.054    8.883    0.000
##    .RCO07             0.504    0.056    8.969    0.000
##    .EN01              0.689    0.071    9.661    0.000
##    .EN02              0.439    0.048    9.066    0.000
##    .EN04              0.476    0.051    9.266    0.000
##    .EN05              0.381    0.043    8.945    0.000
##    .EN06              0.367    0.041    8.925    0.000
##    .EN07              0.502    0.055    9.210    0.000
##    .EN08              0.358    0.041    8.708    0.000
##    .EVI01             0.177    0.036    4.919    0.000
##    .EVI02             0.242    0.038    6.298    0.000
##    .EVI03             1.222    0.124    9.826    0.000
##    .EDE01             0.395    0.065    6.060    0.000
##    .EDE02             0.498    0.066    7.579    0.000
##    .EDE03             0.836    0.085    9.887    0.000
##    .EAB01             0.478    0.099    4.805    0.000
##    .EAB02             1.010    0.109    9.283    0.000
##    .EAB03             1.718    0.176    9.778    0.000
##     recuperacion      0.972    0.199    4.896    0.000
lavaanPlot(cfa34, coef=TRUE, cov=TRUE)
summary(cfa34, fit.measures=TRUE)
## lavaan 0.6-19 ended normally after 90 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       108
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                              2445.310
##   Degrees of freedom                              1020
##   P-value (Chi-square)                           0.000
## 
## Model Test Baseline Model:
## 
##   Test statistic                             13350.303
##   Degrees of freedom                              1081
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    0.884
##   Tucker-Lewis Index (TLI)                       0.877
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -15426.580
##   Loglikelihood unrestricted model (H1)     -14203.926
##                                                       
##   Akaike (AIC)                               31069.161
##   Bayesian (BIC)                             31437.135
##   Sample-size adjusted Bayesian (SABIC)      31094.870
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.079
##   90 Percent confidence interval - lower         0.075
##   90 Percent confidence interval - upper         0.083
##   P-value H_0: RMSEA <= 0.050                    0.000
##   P-value H_0: RMSEA >= 0.080                    0.369
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.070
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   desapego =~                                         
##     RPD01             1.000                           
##     RPD02             1.209    0.081   14.858    0.000
##     RPD03             1.144    0.085   13.413    0.000
##     RPD05             1.313    0.086   15.311    0.000
##     RPD06             1.083    0.089   12.218    0.000
##     RPD07             1.229    0.085   14.481    0.000
##     RPD08             1.157    0.086   13.376    0.000
##     RPD09             1.316    0.087   15.162    0.000
##     RPD10             1.343    0.088   15.247    0.000
##   relajacion =~                                       
##     RRE02             1.000                           
##     RRE03             1.121    0.065   17.303    0.000
##     RRE04             1.020    0.058   17.611    0.000
##     RRE05             1.051    0.056   18.690    0.000
##     RRE06             1.245    0.074   16.916    0.000
##     RRE07             1.122    0.071   15.848    0.000
##     RRE10             0.815    0.067   12.147    0.000
##   dominio =~                                          
##     RMA02             1.000                           
##     RMA03             1.152    0.096   12.038    0.000
##     RMA04             1.178    0.089   13.262    0.000
##     RMA05             1.141    0.087   13.054    0.000
##     RMA06             0.648    0.075    8.623    0.000
##     RMA07             1.104    0.085   13.062    0.000
##     RMA08             1.110    0.085   13.002    0.000
##     RMA09             1.030    0.084   12.257    0.000
##     RMA10             1.056    0.088   12.047    0.000
##   control =~                                          
##     RCO02             1.000                           
##     RCO03             0.946    0.049   19.158    0.000
##     RCO04             0.794    0.044   18.081    0.000
##     RCO05             0.815    0.043   18.912    0.000
##     RCO06             0.837    0.046   18.395    0.000
##     RCO07             0.837    0.046   18.199    0.000
##   recuperacion =~                                     
##     desapego          1.000                           
##     relajacion        1.071    0.121    8.858    0.000
##     dominio           0.900    0.129    6.965    0.000
##     control           1.421    0.157    9.066    0.000
##   energia =~                                          
##     EN01              1.000                           
##     EN02              1.026    0.044   23.558    0.000
##     EN04              0.996    0.043   22.912    0.000
##     EN05              0.994    0.042   23.892    0.000
##     EN06              0.981    0.041   23.944    0.000
##     EN07              1.044    0.045   23.105    0.000
##     EN08              1.031    0.042   24.449    0.000
##   vigor =~                                            
##     EVI01             1.000                           
##     EVI02             0.978    0.027   35.896    0.000
##     EVI03             0.990    0.048   20.656    0.000
##   dedicacion =~                                       
##     EDE01             1.000                           
##     EDE02             0.913    0.035   26.219    0.000
##     EDE03             0.580    0.037   15.851    0.000
##   absorcion =~                                        
##     EAB01             1.000                           
##     EAB02             0.707    0.051   13.915    0.000
##     EAB03             0.730    0.063   11.619    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   vigor ~~                                            
##     dedicacion        2.767    0.293    9.427    0.000
##     absorcion         2.132    0.248    8.613    0.000
##   dedicacion ~~                                       
##     absorcion         2.731    0.293    9.316    0.000
##   recuperacion ~~                                     
##     energia           1.367    0.197    6.938    0.000
##     vigor             1.007    0.165    6.098    0.000
##     dedicacion        1.049    0.179    5.852    0.000
##     absorcion         0.796    0.151    5.281    0.000
##   energia ~~                                          
##     vigor             2.045    0.249    8.223    0.000
##     dedicacion        1.852    0.259    7.139    0.000
##     absorcion         1.340    0.220    6.091    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .desapego          0.951    0.149    6.400    0.000
##    .relajacion        0.510    0.085    6.021    0.000
##    .dominio           1.191    0.200    5.958    0.000
##    .control           0.699    0.125    5.583    0.000
##     energia           2.823    0.327    8.623    0.000
##     vigor             2.859    0.289    9.900    0.000
##     absorcion         2.595    0.301    8.628    0.000
##     dedicacion        3.458    0.367    9.424    0.000
##    .RPD01             1.169    0.120    9.782    0.000
##    .RPD02             0.984    0.107    9.204    0.000
##    .RPD03             1.435    0.147    9.730    0.000
##    .RPD05             0.973    0.109    8.940    0.000
##    .RPD06             1.835    0.184    9.979    0.000
##    .RPD07             1.166    0.124    9.378    0.000
##    .RPD08             1.485    0.152    9.739    0.000
##    .RPD09             1.036    0.115    9.034    0.000
##    .RPD10             1.044    0.116    8.982    0.000
##    .RRE02             0.623    0.067    9.253    0.000
##    .RRE03             0.646    0.072    8.974    0.000
##    .RRE04             0.494    0.056    8.837    0.000
##    .RRE05             0.384    0.047    8.203    0.000
##    .RRE06             0.882    0.097    9.126    0.000
##    .RRE07             0.929    0.098    9.458    0.000
##    .RRE10             1.134    0.112   10.086    0.000
##    .RMA02             1.742    0.175    9.935    0.000
##    .RMA03             1.500    0.156    9.595    0.000
##    .RMA04             0.857    0.098    8.786    0.000
##    .RMA05             0.904    0.101    8.985    0.000
##    .RMA06             1.626    0.158   10.280    0.000
##    .RMA07             0.843    0.094    8.978    0.000
##    .RMA08             0.881    0.098    9.029    0.000
##    .RMA09             1.089    0.115    9.498    0.000
##    .RMA10             1.256    0.131    9.591    0.000
##    .RCO02             0.980    0.104    9.394    0.000
##    .RCO03             0.493    0.058    8.473    0.000
##    .RCO04             0.468    0.052    9.019    0.000
##    .RCO05             0.393    0.046    8.620    0.000
##    .RCO06             0.479    0.054    8.883    0.000
##    .RCO07             0.504    0.056    8.969    0.000
##    .EN01              0.689    0.071    9.661    0.000
##    .EN02              0.439    0.048    9.066    0.000
##    .EN04              0.476    0.051    9.266    0.000
##    .EN05              0.381    0.043    8.945    0.000
##    .EN06              0.367    0.041    8.925    0.000
##    .EN07              0.502    0.055    9.210    0.000
##    .EN08              0.358    0.041    8.708    0.000
##    .EVI01             0.177    0.036    4.919    0.000
##    .EVI02             0.242    0.038    6.298    0.000
##    .EVI03             1.222    0.124    9.826    0.000
##    .EDE01             0.395    0.065    6.060    0.000
##    .EDE02             0.498    0.066    7.579    0.000
##    .EDE03             0.836    0.085    9.887    0.000
##    .EAB01             0.478    0.099    4.805    0.000
##    .EAB02             1.010    0.109    9.283    0.000
##    .EAB03             1.718    0.176    9.778    0.000
##     recuperacion      0.972    0.199    4.896    0.000
# Revisar los valores de comparative fit index y tucker lewis index
# Excelente si es >= 0.95, aceptable entre 0.9 y 0.95, debieciente menor a 0.90
---
title: "Act 2 - GEFA"
author: "Jenaro Martínez A01721951"
date: "2025-02-17"
output: 
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
    theme: cerulean
---

# <span style = "color:blue;">Teoría</span>
Los *Modelos de Ecuaciones Estructurales (SEM)* son una técnica de análisis de estadística multivariada que permite analizar patrones complejos de relaciones entre variables, realizar comparaciones entre e intra grupos, y validar modelos teóricos y empíricos.

# <span style = "color:blue;">Ejemplo 1. Estudio de Holzinger y Swineford (1939)</span>

## <span style = "color:blue;">Contexto</span>
Holzinger y Swineford realizaron exámenes de habilidad mental a adolescentes de 7º y 8º grado de dos escuelas (Pasteur y Grant-White).

La base de datos está incluida como paquete en R, e incluye las siguientes columnas


* sex: Género (1=male, 2=female)
* x1: Percepción visual
* x2: Juego de cubos
* x3: Juego con pastillas/especial
+ x4: Comprensión de párrafos
* x5: Completar oraciones
* x6: Significado de palabras
* x7: Sumas aceleradas
* x8: Conteo acelerado de puntos
* x9: Discriminación acelerada de mayúsculas rectas y curvas

Se busca identificar las relaciones entre las habilidades visuales (x1, x2, x3), textual (x4, x5, x6) y velocidad (x7, x8, x9) de los adolecentes

# Instalar paquetes y llamar librerias
```{r message=FALSE, warning=FALSE}
# install.packages("lavaan") #latent variable analisis
# install.packages("lavaanPlot")

library(lavaan)
library(lavaanPlot)
```

## <span style="color: blue;> Entender la base de datos</span>
```{r}
df1 <- HolzingerSwineford1939

summary(df1)
head(df1)
```
## <span style="color: blue;>  Tipos de formulas </span>
1. Regresión (~) Variable que depende de otras
2. Variables Latentes (=~) No se observa, se infiere.
3. Variablew y Covarianzas (~~) Relaciones entre variables latentes y observada (Varianza: entre sí misma, Covarianza: entre otras).
4. Intercepto (~1) Valor esperado cuando las demás variables son cero.

## <span style="color: blue;> Estructurar el modelo </span>
```{r}
modelo1 <- ' # Regresiones
            # Variables Latentes  
            visual =~ x1 + x2 + x3
            textual =~ x4+ x5 +x6
            velocidad =~ x7 + x8 +x9
            # Varianzas y Covarianzas
            visual ~~ visual
            textual ~~ textual
            velocidad ~~ velocidad
            visual ~~ textual + velocidad
            textual ~~ velocidad
            # Intercepto
          '
```

## <span style="color: blue;> Generar el Análisis Factorial Confrimatorio </span>
```{r}
cfa1 <- sem(modelo1, data=df1)
summary(cfa1)
lavaanPlot(cfa1, coef=TRUE, cov=TRUE)
```

# Actividad 3. Aplicación de modelos de ecuaciones estructurales

```{r message=FALSE, warning=FALSE}
library(readxl)
library(lavaan)
library(lavaanPlot)
```

```{r}
# file.choose()
df3 <- read_excel("/Users/jenaromtzg/Desktop/Generación de Escenarios Futuros/R/Semana 2/Datos_SEM_Eng.xlsx")
```

```{r}
summary(df3)
str(df3)
head(df3)
```

## <span style="color: blue;> Estructurar el modelo </span>
```{r}
modelo31 <- ' # Regresiones
            # Variables Latentes  
            desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
            relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
            dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
            control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
            recuperacion =~ desapego + relajacion + dominio + control
            # Varianzas y Covarianzas
            desapego ~~ desapego
            relajacion ~~ relajacion
            dominio ~~ dominio
            control ~~ control
            # Intercepto
          '
```

## <span style="color: blue;> Generar el Análisis Factorial Confrimatorio </span>
```{r}
cfa31 <- sem(modelo31, data=df3)
summary(cfa31)
lavaanPlot(cfa31, coef=TRUE, cov=TRUE)
```

```{r}
modelo32 <- ' # Regresiones
            # Variables Latentes  
            energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
  
            # Varianzas y Covarianzas
            energia ~~ energia

            # Intercepto
          '
```

## <span style="color: blue;> Generar el Análisis Factorial Confrimatorio </span>
```{r}
cfa32 <- sem(modelo32, data=df3)
summary(cfa32)
lavaanPlot(cfa32, coef=TRUE, cov=TRUE)
```

```{r}
summary(cfa32, fit.measures=TRUE)

# Revisar los valores de comparative fit index y tucker lewis index
# Excelente si es >= 0.95, aceptable entre 0.9 y 0.95, debieciente menor a 0.90
```

```{r}
modelo33 <- ' # Regresiones
            # Variables Latentes  
            vigor =~ EVI01 + EVI02 + EVI03
            dedicacion =~ EDE01 + EDE02 + EDE03
            absorcion =~ EAB01 + EAB02 + EAB03
            
            # Varianzas y Covarianzas
            vigor ~~ vigor
            absorcion ~~ absorcion
            dedicacion ~~ dedicacion
            vigor ~~ dedicacion + absorcion
            dedicacion ~~ absorcion
            # Intercepto
          '
```

## <span style="color: blue;> Generar el Análisis Factorial Confrimatorio </span>
```{r}
cfa33 <- sem(modelo33, data=df3)
summary(cfa33)
lavaanPlot(cfa33, coef=TRUE, cov=TRUE)
```
```{r}
summary(cfa33, fit.measures=TRUE)

# Revisar los valores de comparative fit index y tucker lewis index
# Excelente si es >= 0.95, aceptable entre 0.9 y 0.95, debieciente menor a 0.90
```


```{r}
modelo34 <- ' # Regresiones
            # Variables Latentes  
            desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
            relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
            dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
            control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
            recuperacion =~ desapego + relajacion + dominio + control
            energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
            vigor =~ EVI01 + EVI02 + EVI03
            dedicacion =~ EDE01 + EDE02 + EDE03
            absorcion =~ EAB01 + EAB02 + EAB03
            
            # Varianzas y Covarianzas
            desapego ~~ desapego
            relajacion ~~ relajacion
            dominio ~~ dominio
            control ~~ control
            energia ~~ energia
            vigor ~~ vigor
            absorcion ~~ absorcion
            dedicacion ~~ dedicacion
            vigor ~~ dedicacion + absorcion
            dedicacion ~~ absorcion
            recuperacion  ~~ energia + vigor + dedicacion + absorcion
            energia ~~ vigor + dedicacion + absorcion
            
            # Intercepto
          '
```

## <span style="color: blue;> Generar el Análisis Factorial Confrimatorio </span>
```{r}
cfa34 <- sem(modelo34, data=df3)
summary(cfa34)
lavaanPlot(cfa34, coef=TRUE, cov=TRUE)
```

```{r}
summary(cfa34, fit.measures=TRUE)

# Revisar los valores de comparative fit index y tucker lewis index
# Excelente si es >= 0.95, aceptable entre 0.9 y 0.95, debieciente menor a 0.90
```