Lavaan, SEM, R square value

Hi all,
I would like to ask for your help in identifying the R square value (i.e., coefficient of determination) in this result. I am trying to compare the hypothesis model and competing model with the R square value, but I couldn't figure out where it is.

Thanks!

Sys.setenv(LANG = "En")

library(lavaan)
#> This is lavaan 0.6-7
#> lavaan is BETA software! Please report any bugs.
library(semPlot)
#> Registered S3 methods overwritten by 'huge':
#>   method    from   
#>   plot.sim  BDgraph
#>   print.sim BDgraph

#built in data
data(PoliticalDemocracy)

model <- '
# measurement model
ind60 =~ x1 + x2 + x3
dem60 =~ y1 + y2 + y3 + y4    
dem65 =~ y5 + y6 + y7 + y8
# regressions
dem60 ~ ind60
dem65 ~ ind60 + dem60
# residual correlations
y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8
'
fit <- sem(model, data=PoliticalDemocracy)
semPaths(fit)

summary(fit, standardized=TRUE, fit.measures=T)
#> lavaan 0.6-7 ended normally after 68 iterations
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of free parameters                         31
#>                                                       
#>   Number of observations                            75
#>                                                       
#> Model Test User Model:
#>                                                       
#>   Test statistic                                38.125
#>   Degrees of freedom                                35
#>   P-value (Chi-square)                           0.329
#> 
#> 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)                    0.995
#>   Tucker-Lewis Index (TLI)                       0.993
#> 
#> Loglikelihood and Information Criteria:
#> 
#>   Loglikelihood user model (H0)              -1547.791
#>   Loglikelihood unrestricted model (H1)      -1528.728
#>                                                       
#>   Akaike (AIC)                                3157.582
#>   Bayesian (BIC)                              3229.424
#>   Sample-size adjusted Bayesian (BIC)         3131.720
#> 
#> Root Mean Square Error of Approximation:
#> 
#>   RMSEA                                          0.035
#>   90 Percent confidence interval - lower         0.000
#>   90 Percent confidence interval - upper         0.092
#>   P-value RMSEA <= 0.05                          0.611
#> 
#> Standardized Root Mean Square Residual:
#> 
#>   SRMR                                           0.044
#> 
#> 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                2.180    0.139   15.742    0.000    1.460    0.973
#>     x3                1.819    0.152   11.967    0.000    1.218    0.872
#>   dem60 =~                                                              
#>     y1                1.000                               2.223    0.850
#>     y2                1.257    0.182    6.889    0.000    2.794    0.717
#>     y3                1.058    0.151    6.987    0.000    2.351    0.722
#>     y4                1.265    0.145    8.722    0.000    2.812    0.846
#>   dem65 =~                                                              
#>     y5                1.000                               2.103    0.808
#>     y6                1.186    0.169    7.024    0.000    2.493    0.746
#>     y7                1.280    0.160    8.002    0.000    2.691    0.824
#>     y8                1.266    0.158    8.007    0.000    2.662    0.828
#> 
#> Regressions:
#>                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
#>   dem60 ~                                                               
#>     ind60             1.483    0.399    3.715    0.000    0.447    0.447
#>   dem65 ~                                                               
#>     ind60             0.572    0.221    2.586    0.010    0.182    0.182
#>     dem60             0.837    0.098    8.514    0.000    0.885    0.885
#> 
#> Covariances:
#>                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
#>  .y1 ~~                                                                 
#>    .y5                0.624    0.358    1.741    0.082    0.624    0.296
#>  .y2 ~~                                                                 
#>    .y4                1.313    0.702    1.871    0.061    1.313    0.273
#>    .y6                2.153    0.734    2.934    0.003    2.153    0.356
#>  .y3 ~~                                                                 
#>    .y7                0.795    0.608    1.308    0.191    0.795    0.191
#>  .y4 ~~                                                                 
#>    .y8                0.348    0.442    0.787    0.431    0.348    0.109
#>  .y6 ~~                                                                 
#>    .y8                1.356    0.568    2.386    0.017    1.356    0.338
#> 
#> Variances:
#>                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
#>    .x1                0.082    0.019    4.184    0.000    0.082    0.154
#>    .x2                0.120    0.070    1.718    0.086    0.120    0.053
#>    .x3                0.467    0.090    5.177    0.000    0.467    0.239
#>    .y1                1.891    0.444    4.256    0.000    1.891    0.277
#>    .y2                7.373    1.374    5.366    0.000    7.373    0.486
#>    .y3                5.067    0.952    5.324    0.000    5.067    0.478
#>    .y4                3.148    0.739    4.261    0.000    3.148    0.285
#>    .y5                2.351    0.480    4.895    0.000    2.351    0.347
#>    .y6                4.954    0.914    5.419    0.000    4.954    0.443
#>    .y7                3.431    0.713    4.814    0.000    3.431    0.322
#>    .y8                3.254    0.695    4.685    0.000    3.254    0.315
#>     ind60             0.448    0.087    5.173    0.000    1.000    1.000
#>    .dem60             3.956    0.921    4.295    0.000    0.800    0.800
#>    .dem65             0.172    0.215    0.803    0.422    0.039    0.039

Created on 2021-02-04 by the reprex package (v0.3.0)

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