library(lavaan)
## This is lavaan 0.6-21
## lavaan is FREE software! Please report any bugs.
library(lavaanPlot)
## Warning: package 'lavaanPlot' was built under R version 4.2.3
# Ejercicio
bd2 <- PoliticalDemocracy
summary(bd2)
## y1 y2 y3 y4
## Min. : 1.250 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.900 1st Qu.: 0.000 1st Qu.: 3.767 1st Qu.: 1.581
## Median : 5.400 Median : 3.333 Median : 6.667 Median : 3.333
## Mean : 5.465 Mean : 4.256 Mean : 6.563 Mean : 4.453
## 3rd Qu.: 7.500 3rd Qu.: 8.283 3rd Qu.:10.000 3rd Qu.: 6.667
## Max. :10.000 Max. :10.000 Max. :10.000 Max. :10.000
## y5 y6 y7 y8
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.692 1st Qu.: 0.000 1st Qu.: 3.478 1st Qu.: 1.301
## Median : 5.000 Median : 2.233 Median : 6.667 Median : 3.333
## Mean : 5.136 Mean : 2.978 Mean : 6.196 Mean : 4.043
## 3rd Qu.: 7.500 3rd Qu.: 4.207 3rd Qu.:10.000 3rd Qu.: 6.667
## Max. :10.000 Max. :10.000 Max. :10.000 Max. :10.000
## x1 x2 x3
## Min. :3.784 Min. :1.386 Min. :1.002
## 1st Qu.:4.477 1st Qu.:3.663 1st Qu.:2.300
## Median :5.075 Median :4.963 Median :3.568
## Mean :5.054 Mean :4.792 Mean :3.558
## 3rd Qu.:5.515 3rd Qu.:5.830 3rd Qu.:4.523
## Max. :6.737 Max. :7.872 Max. :6.425
modelo2 <- '
#Regresion~
#Variable latente =~
ind60 =~ x1 + x2 + x3
dem60 =~ y1 + y2 + y3 + y4
dem65 =~ y5 + y6 + y7 + y8
# varianzas y covarianzas ~~
y1 ~~ y5
y2 ~~ y6
y3 ~~ y7
y4 ~~ y8
#Intercepto ~1
'
fit2 <- cfa (modelo2, bd2)
summary (fit2, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-21 ended normally after 67 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 29
##
## Number of observations 75
##
## Model Test User Model:
##
## Test statistic 50.835
## Degrees of freedom 37
## P-value (Chi-square) 0.064
##
## Model Test Baseline Model:
##
## Test statistic 730.654
## Degrees of freedom 55
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.980
## Tucker-Lewis Index (TLI) 0.970
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1554.146
## Loglikelihood unrestricted model (H1) -1528.728
##
## Akaike (AIC) 3166.292
## Bayesian (BIC) 3233.499
## Sample-size adjusted Bayesian (SABIC) 3142.099
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.071
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.115
## P-value H_0: RMSEA <= 0.050 0.234
## P-value H_0: RMSEA >= 0.080 0.396
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.050
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ind60 =~
## x1 1.000 0.670 0.920
## x2 2.181 0.139 15.720 0.000 1.460 0.973
## x3 1.819 0.152 11.966 0.000 1.218 0.872
## dem60 =~
## y1 1.000 2.145 0.824
## y2 1.388 0.188 7.401 0.000 2.977 0.760
## y3 1.053 0.161 6.552 0.000 2.259 0.694
## y4 1.368 0.153 8.928 0.000 2.933 0.881
## dem65 =~
## y5 1.000 2.014 0.777
## y6 1.317 0.180 7.314 0.000 2.654 0.790
## y7 1.326 0.174 7.618 0.000 2.672 0.817
## y8 1.391 0.171 8.118 0.000 2.803 0.870
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .y1 ~~
## .y5 0.892 0.366 2.433 0.015 0.892 0.370
## .y2 ~~
## .y6 1.893 0.762 2.486 0.013 1.893 0.361
## .y3 ~~
## .y7 1.268 0.623 2.035 0.042 1.268 0.287
## .y4 ~~
## .y8 0.141 0.464 0.303 0.762 0.141 0.056
## ind60 ~~
## dem60 0.643 0.202 3.180 0.001 0.448 0.448
## dem65 0.752 0.204 3.688 0.000 0.557 0.557
## dem60 ~~
## dem65 4.079 0.931 4.382 0.000 0.944 0.944
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .x1 0.082 0.020 4.177 0.000 0.082 0.154
## .x2 0.120 0.070 1.708 0.088 0.120 0.053
## .x3 0.467 0.090 5.172 0.000 0.467 0.239
## .y1 2.181 0.456 4.779 0.000 2.181 0.322
## .y2 6.490 1.231 5.271 0.000 6.490 0.423
## .y3 5.490 0.991 5.538 0.000 5.490 0.518
## .y4 2.470 0.660 3.741 0.000 2.470 0.223
## .y5 2.662 0.506 5.260 0.000 2.662 0.396
## .y6 4.249 0.817 5.201 0.000 4.249 0.376
## .y7 3.560 0.712 4.999 0.000 3.560 0.333
## .y8 2.531 0.609 4.159 0.000 2.531 0.244
## ind60 0.448 0.087 5.171 0.000 1.000 1.000
## dem60 4.601 1.084 4.243 0.000 1.000 1.000
## dem65 4.058 1.039 3.907 0.000 1.000 1.000
lavaanPlot(model = fit2, coefs = TRUE, covs = TRUE)